Publications

You can also look at my Google Scholar profile.

2024

1. Lingzi Hong, Pengcheng Luo, Eduardo Blanco, and Xiaoying Song. 2024. Outcome-Constrained Large Language Models for Countering Hate Speech. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 4523–4536, Miami, Florida, USA. Association for Computational Linguistics.

   @inproceedings{hong-etal-2024-outcome,
     title = {Outcome-Constrained Large Language Models for Countering Hate Speech},
     author = {Hong, Lingzi and Luo, Pengcheng and Blanco, Eduardo and Song, Xiaoying},
     editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
     booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
     month = nov,
     year = {2024},
     address = {Miami, Florida, USA},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.emnlp-main.260},
     pages = {4523--4536},
     abstract = {Automatic counterspeech generation methods have been developed to assist efforts in combating hate speech. Existing research focuses on generating counterspeech with linguistic attributes such as being polite, informative, and intent-driven. However, the real impact of counterspeech in online environments is seldom considered. This study aims to develop methods for generating counterspeech constrained by conversation outcomes and evaluate their effectiveness. We experiment with large language models (LLMs) to incorporate into the text generation process two desired conversation outcomes: low conversation incivility and non-hateful hater reentry. Specifically, we experiment with instruction prompts, LLM finetuning, and LLM reinforcement learning (RL). Evaluation results show that our methods effectively steer the generation of counterspeech toward the desired outcomes. Our analyses, however, show that there are differences in the quality and style depending on the model.},
     month_numeric = {11}
   }
   

2. Jiangshu Du, Yibo Wang, Wenting Zhao, Zhongfen Deng, Shuaiqi Liu, Renze Lou, Henry Zou, Pranav Narayanan Venkit, Nan Zhang, Mukund Srinath, Haoran Zhang, Vipul Gupta, Yinghui Li, Tao Li, Fei Wang, Qin Liu, Tianlin Liu, Pengzhi Gao, Congying Xia, Chen Xing, Cheng Jiayang, Zhaowei Wang, Ying Su, Raj Shah, Ruohao Guo, Jing Gu, Haoran Li, Kangda Wei, Zihao Wang, Lu Cheng, Surangika Ranathunga, Meng Fang, Jie Fu, Fei Liu, Ruihong Huang, Eduardo Blanco, Yixin Cao, Rui Zhang, Philip Yu, and Wenpeng Yin. 2024. LLMs Assist NLP Researchers: Critique Paper (Meta-)Reviewing. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 5081–5099, Miami, Florida, USA. Association for Computational Linguistics.

   @inproceedings{du-etal-2024-llms,
     title = {{LLM}s Assist {NLP} Researchers: Critique Paper (Meta-)Reviewing},
     author = {Du, Jiangshu and Wang, Yibo and Zhao, Wenting and Deng, Zhongfen and Liu, Shuaiqi and Lou, Renze and Zou, Henry and Narayanan Venkit, Pranav and Zhang, Nan and Srinath, Mukund and Zhang, Haoran and Gupta, Vipul and Li, Yinghui and Li, Tao and Wang, Fei and Liu, Qin and Liu, Tianlin and Gao, Pengzhi and Xia, Congying and Xing, Chen and Jiayang, Cheng and Wang, Zhaowei and Su, Ying and Shah, Raj and Guo, Ruohao and Gu, Jing and Li, Haoran and Wei, Kangda and Wang, Zihao and Cheng, Lu and Ranathunga, Surangika and Fang, Meng and Fu, Jie and Liu, Fei and Huang, Ruihong and Blanco, Eduardo and Cao, Yixin and Zhang, Rui and Yu, Philip and Yin, Wenpeng},
     editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
     booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
     month = nov,
     year = {2024},
     address = {Miami, Florida, USA},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.emnlp-main.292},
     pages = {5081--5099},
     abstract = {Claim: This work is not advocating the use of LLMs for paper (meta-)reviewing. Instead, wepresent a comparative analysis to identify and distinguish LLM activities from human activities. Two research goals: i) Enable better recognition of instances when someone implicitly uses LLMs for reviewing activities; ii) Increase community awareness that LLMs, and AI in general, are currently inadequate for performing tasks that require a high level of expertise and nuanced judgment.This work is motivated by two key trends. On one hand, large language models (LLMs) have shown remarkable versatility in various generative tasks such as writing, drawing, and question answering, significantly reducing the time required for many routine tasks. On the other hand, researchers, whose work is not only time-consuming but also highly expertise-demanding, face increasing challenges as they have to spend more time reading, writing, and reviewing papers. This raises the question: how can LLMs potentially assist researchers in alleviating their heavy workload?This study focuses on the topic of LLMs as NLP Researchers, particularly examining the effectiveness of LLMs in assisting paper (meta-)reviewing and its recognizability. To address this, we constructed the ReviewCritique dataset, which includes two types of information: (i) NLP papers (initial submissions rather than camera-ready) with both human-written and LLM-generated reviews, and (ii) each review comes with {``}deficiency{''} labels and corresponding explanations for individual segments, annotated by experts. Using ReviewCritique, this study explores two threads of research questions: (i) {``}LLMs as Reviewers{''}, how do reviews generated by LLMs compare with those written by humans in terms of quality and distinguishability? (ii) {``}LLMs as Metareviewers{''}, how effectively can LLMs identify potential issues, such as Deficient or unprofessional review segments, within individual paper reviews? To our knowledge, this is the first work to provide such a comprehensive analysis.},
     month_numeric = {11}
   }
   

3. Mayank Singh and Eduardo Blanco. 2024. Learning to Generate Rules for Realistic Few-Shot Relation Classification: An Encoder-Decoder Approach. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 5907–5921, Miami, Florida, USA. Association for Computational Linguistics.

   @inproceedings{singh-blanco-2024-learning,
     title = {Learning to Generate Rules for Realistic Few-Shot Relation Classification: An Encoder-Decoder Approach},
     author = {Singh, Mayank and Blanco, Eduardo},
     editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
     month = nov,
     year = {2024},
     address = {Miami, Florida, USA},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.findings-emnlp.341},
     pages = {5907--5921},
     abstract = {We propose a neuro-symbolic approach for realistic few-shot relation classification via rules. Instead of building neural models to predict relations, we design them to output straightforward rules that can be used to extract relations. The rules are generated using custom T5-style Encoder-Decoder Language Models. Crucially, our rules are fully interpretable and pliable (i.e., humans can easily modify them to boost performance). Through a combination of rules generated by these models along with a very effective, novel baseline, we demonstrate a few-shot relation-classification performance that is comparable to or stronger than the state of the art on the Few-Shot TACRED and NYT29 benchmarks while increasing interpretability and maintaining pliability.},
     month_numeric = {11}
   }
   

4. Yash Mahajan, Naman Bansal, Eduardo Blanco, and Santu Karmaker. 2024. ALIGN-SIM: A Task-Free Test Bed for Evaluating and Interpreting Sentence Embeddings through Semantic Similarity Alignment. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 7393–7428, Miami, Florida, USA. Association for Computational Linguistics.

   @inproceedings{mahajan-etal-2024-align,
     title = {{ALIGN}-{SIM}: A Task-Free Test Bed for Evaluating and Interpreting Sentence Embeddings through Semantic Similarity Alignment},
     author = {Mahajan, Yash and Bansal, Naman and Blanco, Eduardo and Karmaker, Santu},
     editor = {Al-Onaizan, Yaser and Bansal, Mohit and Chen, Yun-Nung},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
     month = nov,
     year = {2024},
     address = {Miami, Florida, USA},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.findings-emnlp.436},
     pages = {7393--7428},
     abstract = {Sentence embeddings play a pivotal role in a wide range of NLP tasks, yet evaluating and interpreting these real-valued vectors remains an open challenge to date, especially in a task-free setting. To address this challenge, we introduce a novel task-free test bed for evaluating and interpreting sentence embeddings. Our test bed consists of five semantic similarity alignment criteria, namely, *semantic distinction, synonym replacement, antonym replacement, paraphrasing without negation, and sentence jumbling*. Using these criteria, we examined five classical (e.g., Sentence-BERT, Universal Sentence Encoder (USE), etc.) and eight LLM-induced sentence embedding techniques (e.g., LLaMA2, GPT-3, OLMo, etc.) to test whether their semantic similarity spaces align with what a human mind would naturally expect. Our extensive experiments with 13 different sentence encoders revealed that none of the studied embeddings aligned with all the five semantic similarity alignment criteria. Yet, most encoders performed highly on the SentEval dataset, a popular task-specific benchmark. This finding demonstrates a significant limitation of the current practice in sentence embedding evaluation and associated popular benchmarks, a critical issue that needs careful attention and reassessment by the NLP community. Finally, we conclude the paper by highlighting the utility of the proposed alignment-based test bed for analyzing sentence embeddings in a novel way, especially in a task-free setting.},
     month_numeric = {11}
   }
   

5. Subash Chandra Pakhrin, Neha Chauhan, Salman Khan, Jamie Upadhyaya, Moriah Rene Beck, and Eduardo Blanco. 2024. Prediction of human O-linked glycosylation sites using stacked generalization and embeddings from pre-trained protein language model. Bioinformatics:btae643.

   @article{10.1093/bioinformatics/btae643,
     author = {Pakhrin, Subash Chandra and Chauhan, Neha and Khan, Salman and Upadhyaya, Jamie and Beck, Moriah Rene and Blanco, Eduardo},
     title = {{Prediction of human O-linked glycosylation sites using stacked generalization and embeddings from pre-trained protein language model}},
     journal = {Bioinformatics},
     pages = {btae643},
     year = {2024},
     month = oct,
     abstract = {{O-linked glycosylation, an essential post-translational modification process in Homo sapiens, involves attaching sugar moieties to the oxygen atoms of serine and/or threonine residues. It influences various biological and cellular functions. While threonine or serine residues within protein sequences are potential sites for O-linked glycosylation, not all serine and/or threonine residues undergo this modification, underscoring the importance of characterizing its occurrence. This study presents a novel approach for predicting intracellular and extracellular O-linked glycosylation events on proteins, which are crucial for comprehending cellular processes. Two base multi-layer perceptron models were trained by leveraging a stacked generalization framework. These base models respectively employ ProtT5 and Ankh O-linked glycosylation site-specific embeddings whose combined predictions are used to train the meta-multi-layer perceptron model. Trained on extensive O-linked glycosylation datasets, the stacked-generalization model demonstrated high predictive performance on independent test datasets. Furthermore, the study emphasizes the distinction between nucleocytoplasmic and extracellular O-linked glycosylation, offering insights into their functional implications that were overlooked in previous studies. By integrating the protein language model’s embedding with stacked generalization techniques, this approach enhances predictive accuracy of O-linked glycosylation events and illuminates the intricate roles of O-linked glycosylation in proteomics, potentially accelerating the discovery of novel glycosylation sites.Stack-OglyPred-PLM produces Sensitivity, Specificity, Matthews Correlation Coefficient, and Accuracy of 90.50\%, 89.60\%, 0.464, and 89.70\%, respectively on a benchmark NetOGlyc-4.0 independent test dataset. These results demonstrate that Stack-OglyPred-PLM is a robust computational tool to predict O-linked glycosylation sites in proteins.The developed tool, programs, training, and test dataset are available at https://github.com/PakhrinLab/Stack-OglyPred-PLMSupplementary data are available at Bioinformatics online.}},
     issn = {1367-4811},
     doi = {10.1093/bioinformatics/btae643},
     url = {https://doi.org/10.1093/bioinformatics/btae643},
     eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btae643/60071984/btae643.pdf},
     month_numeric = {10}
   }
   

6. MohammadHossein Rezaei and Eduardo Blanco. 2024. Paraphrasing in Affirmative Terms Improves Negation Understanding. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 602–615, Bangkok, Thailand. Association for Computational Linguistics.

   @inproceedings{rezaei-blanco-2024-paraphrasing,
     title = {Paraphrasing in Affirmative Terms Improves Negation Understanding},
     author = {Rezaei, MohammadHossein and Blanco, Eduardo},
     editor = {Ku, Lun-Wei and Martins, Andre and Srikumar, Vivek},
     booktitle = {Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
     month = aug,
     year = {2024},
     address = {Bangkok, Thailand},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.acl-short.55},
     doi = {10.18653/v1/2024.acl-short.55},
     pages = {602--615},
     abstract = {Negation is a common linguistic phenomenon. Yet language models face challenges with negation in many natural language understanding tasks such as question answering and natural language inference. In this paper, we experiment with seamless strategies that incorporate affirmative interpretations (i.e., paraphrases without negation) to make models more robust against negation. Crucially, our affirmative interpretations are obtained automatically. We show improvements with CondaQA, a large corpus requiring reasoning with negation, and five natural language understanding tasks.},
     month_numeric = {8}
   }
   

7. Md Nayem Uddin, Enfa George, Eduardo Blanco, and Steven Corman. 2024. Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pages 5612–5627, Mexico City, Mexico. Association for Computational Linguistics.

   @inproceedings{uddin-etal-2024-generating,
     title = {Generating Uncontextualized and Contextualized Questions for Document-Level Event Argument Extraction},
     author = {Uddin, Md Nayem and George, Enfa and Blanco, Eduardo and Corman, Steven},
     editor = {Duh, Kevin and Gomez, Helena and Bethard, Steven},
     booktitle = {Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
     month = jun,
     year = {2024},
     address = {Mexico City, Mexico},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.naacl-long.312},
     pages = {5612--5627},
     abstract = {This paper presents multiple question generation strategies for document-level event argument extraction. These strategies do not require human involvement and result in uncontextualized questions as well as contextualized questions grounded on the event and document of interest. Experimental results show that combining uncontextualized and contextualized questions is beneficial,especially when event triggers and arguments appear in different sentences. Our approach does not have corpus-specific components, in particular, the question generation strategies transfer across corpora. We also present a qualitative analysis of the most common errors made by our best model.},
     month_numeric = {6}
   }
   

8. Zijie Wang, Farzana Rashid, and Eduardo Blanco. 2024. Interpreting Answers to Yes-No Questions in Dialogues from Multiple Domains. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 2111–2128, Mexico City, Mexico. Association for Computational Linguistics.

   @inproceedings{wang-etal-2024-interpreting,
     title = {Interpreting Answers to Yes-No Questions in Dialogues from Multiple Domains},
     author = {Wang, Zijie and Rashid, Farzana and Blanco, Eduardo},
     editor = {Duh, Kevin and Gomez, Helena and Bethard, Steven},
     booktitle = {Findings of the Association for Computational Linguistics: NAACL 2024},
     month = jun,
     year = {2024},
     address = {Mexico City, Mexico},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.findings-naacl.136},
     pages = {2111--2128},
     abstract = {People often answer yes-no questions without explicitly saying yes, no, or similar polar key-words. Figuring out the meaning of indirectanswers is challenging, even for large language models. In this paper, we investigate this problem working with dialogues from multiple domains. We present new benchmarks in three diverse domains: movie scripts, tennis interviews, and airline customer service. We present an approach grounded on distant supervision and blended training to quickly adapt to a new dialogue domain. Experimental results show that our approach is never detrimental and yields F1 improvements as high as 11-34{\%}.},
     month_numeric = {6}
   }
   

9. Javad Rafiei Asl, Prajwal Panzade, Eduardo Blanco, Daniel Takabi, and Zhipeng Cai. 2024. RobustSentEmbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3795–3809, Mexico City, Mexico. Association for Computational Linguistics.

   @inproceedings{rafiei-asl-etal-2024-robustsentembed,
     title = {{R}obust{S}ent{E}mbed: Robust Sentence Embeddings Using Adversarial Self-Supervised Contrastive Learning},
     author = {Rafiei Asl, Javad and Panzade, Prajwal and Blanco, Eduardo and Takabi, Daniel and Cai, Zhipeng},
     editor = {Duh, Kevin and Gomez, Helena and Bethard, Steven},
     booktitle = {Findings of the Association for Computational Linguistics: NAACL 2024},
     month = jun,
     year = {2024},
     address = {Mexico City, Mexico},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2024.findings-naacl.241},
     pages = {3795--3809},
     abstract = {Pre-trained language models (PLMs) have consistently demonstrated outstanding performance across a diverse spectrum of natural language processing tasks. Nevertheless, despite their success with unseen data, current PLM-based representations often exhibit poor robustness in adversarial settings. In this paper, we introduce RobustSentEmbed, a self-supervised sentence embedding framework designed to improve both generalization and robustness in diverse text representation tasks and against a diverse set of adversarial attacks. Through the generation of high-risk adversarial perturbations and their utilization in a novel objective function, RobustSentEmbed adeptly learns high-quality and robust sentence embeddings. Our experiments confirm the superiority of RobustSentEmbed over state-of-the-art representations. Specifically, Our framework achieves a significant reduction in the success rate of various adversarial attacks, notably reducing the BERTAttack success rate by almost half (from 75.51{\%} to 38.81{\%}). The framework also yields improvements of 1.59{\%} and 0.23{\%} in semantic textual similarity tasks and various transfer tasks, respectively.},
     month_numeric = {6}
   }
   

10. Zhaomin Xiao, Yan Huang, and Eduardo Blanco. 2024. Analyzing Large Language Models’ Capability in Location Prediction. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 951–958, Torino, Italia. ELRA and ICCL.

    @inproceedings{xiao-etal-2024-analyzing-large,
      title = {Analyzing Large Language Models{'} Capability in Location Prediction},
      author = {Xiao, Zhaomin and Huang, Yan and Blanco, Eduardo},
      editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
      booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
      month = may,
      year = {2024},
      address = {Torino, Italia},
      publisher = {ELRA and ICCL},
      url = {https://aclanthology.org/2024.lrec-main.85},
      pages = {951--958},
      abstract = {In this paper, we investigate and evaluate large language models{'} capability in location prediction. We present experimental results with four models{---}FLAN-T5, FLAN-UL2, FLAN-Alpaca, and ChatGPT{---}in various instruction finetuning and exemplar settings. We analyze whether taking into account the context{---}tweets published before and after the tweet mentioning a location{---}is beneficial. Additionally, we conduct an ablation study to explore whether instruction modification is beneficial. Lastly, our qualitative analysis sheds light on the errors made by the best-performing model.},
      month_numeric = {5}
    }
    

11. Md Nayem Uddin, Enfa Rose George, Eduardo Blanco, and Steven R. Corman. 2024. Asking and Answering Questions to Extract Event-Argument Structures. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 1609–1626, Torino, Italia. ELRA and ICCL.

    @inproceedings{uddin-etal-2024-asking-answering,
      title = {Asking and Answering Questions to Extract Event-Argument Structures},
      author = {Uddin, Md Nayem and George, Enfa Rose and Blanco, Eduardo and Corman, Steven R.},
      editor = {Calzolari, Nicoletta and Kan, Min-Yen and Hoste, Veronique and Lenci, Alessandro and Sakti, Sakriani and Xue, Nianwen},
      booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
      month = may,
      year = {2024},
      address = {Torino, Italia},
      publisher = {ELRA and ICCL},
      url = {https://aclanthology.org/2024.lrec-main.142},
      pages = {1609--1626},
      abstract = {This paper presents a question-answering approach to extract document-level event-argument structures. We automatically ask and answer questions for each argument type an event may have. Questions are generated using manually defined templates and generative transformers. Template-based questions are generated using predefined role-specific wh-words and event triggers from the context document. Transformer-based questions are generated using large language models trained to formulate questions based on a passage and the expected answer. Additionally, we develop novel data augmentation strategies specialized in inter-sentential event-argument relations. We use a simple span-swapping technique, coreference resolution, and large language models to augment the training instances. Our approach enables transfer learning without any corpora-specific modifications and yields competitive results with the RAMS dataset. It outperforms previous work, and it is especially beneficial to extract arguments that appear in different sentences than the event trigger. We also present detailed quantitative and qualitative analyses shedding light on the most common errors made by our best model.},
      month_numeric = {5}
    }
    

12. Xinchen Yu, Eduardo Blanco, and Lingzi Hong. 2024. Hate Cannot Drive Out Hate: Forecasting Conversation Incivility following Replies to Hate Speech. Proceedings of the International AAAI Conference on Web and Social Media, 18(1):1740–1752.

    @article{Yu_Blanco_Hong_2024,
      title = {Hate Cannot Drive Out Hate: Forecasting Conversation Incivility following Replies to Hate Speech},
      volume = {18},
      url = {https://ojs.aaai.org/index.php/ICWSM/article/view/31422},
      doi = {10.1609/icwsm.v18i1.31422},
      abstractnote = {User-generated counter hate speech is a promising means to combat hate speech, but questions about whether it can stop incivility in follow-up conversations linger. We argue that effective counter hate speech stops incivility from emerging in follow-up conversations—counter hate that elicits more incivility is counterproductive. This study introduces the task of predicting the incivility of conversations following replies to hate speech. We first propose a metric to measure conversation incivility based on the number of civil and uncivil comments as well as the unique authors involved in the discourse. Our metric approximates human judgments more accurately than previous metrics. We then use the metric to evaluate the outcomes of replies to hate speech. A linguistic analysis uncovers the differences in the language of replies that elicit follow-up conversations with high and low incivility. Experimental results show that forecasting incivility is challenging. We close with a qualitative analysis shedding light into the most common errors made by the best model.},
      number = {1},
      journal = {Proceedings of the International AAAI Conference on Web and Social Media},
      author = {Yu, Xinchen and Blanco, Eduardo and Hong, Lingzi},
      year = {2024},
      month = may,
      pages = {1740-1752},
      month_numeric = {5}
    }
    

13. Andrew Vargas Palacios, Pujan Acharya, Anthony Stephen Peidl, Moriah Rene Beck, Eduardo Blanco, Avdesh Mishra, Tasneem Bawa-Khalfe, and Subash Chandra Pakhrin. 2024. SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model. NAR Genomics and Bioinformatics, 6(1):lqae011.

    @article{10.1093/nargab/lqae011,
      author = {Palacios, Andrew Vargas and Acharya, Pujan and Peidl, Anthony Stephen and Beck, Moriah Rene and Blanco, Eduardo and Mishra, Avdesh and Bawa-Khalfe, Tasneem and Pakhrin, Subash Chandra},
      title = {{SumoPred-PLM: human SUMOylation and SUMO2/3 sites Prediction using Pre-trained Protein Language Model}},
      journal = {NAR Genomics and Bioinformatics},
      volume = {6},
      number = {1},
      pages = {lqae011},
      year = {2024},
      month = feb,
      abstract = {{SUMOylation is an essential post-translational modification system with the ability to regulate nearly all aspects of cellular physiology. Three major paralogues SUMO1, SUMO2 and SUMO3 form a covalent bond between the small ubiquitin-like modifier with lysine residues at consensus sites in protein substrates. Biochemical studies continue to identify unique biological functions for protein targets conjugated to SUMO1 versus the highly homologous SUMO2 and SUMO3 paralogues. Yet, the field has failed to harness contemporary AI approaches including pre-trained protein language models to fully expand and/or recognize the SUMOylated proteome. Herein, we present a novel, deep learning-based approach called SumoPred-PLM for human SUMOylation prediction with sensitivity, specificity, Matthew's correlation coefficient, and accuracy of 74.64\\%, 73.36\\%, 0.48\\% and 74.00\\%, respectively, on the CPLM 4.0 independent test dataset. In addition, this novel platform uses contextualized embeddings obtained from a pre-trained protein language model, ProtT5-XL-UniRef50 to identify SUMO2/3-specific conjugation sites. The results demonstrate that SumoPred-PLM is a powerful and unique computational tool to predict SUMOylation sites in proteins and accelerate discovery.}},
      issn = {2631-9268},
      doi = {10.1093/nargab/lqae011},
      url = {https://doi.org/10.1093/nargab/lqae011},
      eprint = {https://academic.oup.com/nargab/article-pdf/6/1/lqae011/56613188/lqae011.pdf},
      month_numeric = {2}
    }
    

2023

1. Xinchen Yu, Ashley Zhao, Eduardo Blanco, and Lingzi Hong. 2023. A Fine-Grained Taxonomy of Replies to Hate Speech. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 7275–7289, Singapore. Association for Computational Linguistics.

   @inproceedings{yu-etal-2023-fine,
     title = {A Fine-Grained Taxonomy of Replies to Hate Speech},
     author = {Yu, Xinchen and Zhao, Ashley and Blanco, Eduardo and Hong, Lingzi},
     editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
     booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
     month = dec,
     year = {2023},
     address = {Singapore},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.emnlp-main.450},
     doi = {10.18653/v1/2023.emnlp-main.450},
     pages = {7275--7289},
     abstract = {Countering rather than censoring hate speech has emerged as a promising strategy to address hatred. There are many types of counterspeech in user-generated content: addressing the hateful content or its author, generic requests, well-reasoned counter arguments, insults, etc. The effectiveness of counterspeech, which we define as subsequent incivility, depends on these types. In this paper, we present a theoretically grounded taxonomy of replies to hate speech and a new corpus. We work with real, user-generated hate speech and all the replies it elicits rather than replies generated by a third party. Our analyses provide insights into the content real users reply with as well as which replies are empirically most effective. We also experiment with models to characterize the replies to hate speech, thereby opening the door to estimating whether a reply to hate speech will result in further incivility.},
     month_numeric = {12}
   }
   

2. Abdullah Albanyan, Ahmed Hassan, and Eduardo Blanco. 2023. Finding Authentic Counterhate Arguments: A Case Study with Public Figures. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13862–13876, Singapore. Association for Computational Linguistics.

   @inproceedings{albanyan-etal-2023-finding,
     title = {Finding Authentic Counterhate Arguments: A Case Study with Public Figures},
     author = {Albanyan, Abdullah and Hassan, Ahmed and Blanco, Eduardo},
     editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
     booktitle = {Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing},
     month = dec,
     year = {2023},
     address = {Singapore},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.emnlp-main.855},
     doi = {10.18653/v1/2023.emnlp-main.855},
     pages = {13862--13876},
     abstract = {We explore authentic counterhate arguments for online hateful content toward individuals. Previous efforts are limited to counterhate to fight against hateful content toward groups. Thus, we present a corpus of 54,816 hateful tweet-paragraph pairs, where the paragraphs are candidate counterhate arguments. The counterhate arguments are retrieved from 2,500 online articles from multiple sources. We propose a methodology that assures the authenticity of the counter argument and its specificity to the individual of interest. We show that finding arguments in online articles is an efficient alternative to counterhate generation approaches that may hallucinate unsupported arguments. We also present linguistic insights on the language used in counterhate arguments. Experimental results show promising results. It is more challenging, however, to identify counterhate arguments for hateful content toward individuals not included in the training set.},
     month_numeric = {12}
   }
   

3. Zijie Wang, Md Hossain, Shivam Mathur, Terry Melo, Kadir Ozler, Keun Park, Jacob Quintero, MohammadHossein Rezaei, Shreya Shakya, Md Uddin, and Eduardo Blanco. 2023. Interpreting Indirect Answers to Yes-No Questions in Multiple Languages. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2210–2227, Singapore. Association for Computational Linguistics.

   @inproceedings{wang-etal-2023-interpreting,
     title = {Interpreting Indirect Answers to Yes-No Questions in Multiple Languages},
     author = {Wang, Zijie and Hossain, Md and Mathur, Shivam and Melo, Terry and Ozler, Kadir and Park, Keun and Quintero, Jacob and Rezaei, MohammadHossein and Shakya, Shreya and Uddin, Md and Blanco, Eduardo},
     editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
     month = dec,
     year = {2023},
     address = {Singapore},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.findings-emnlp.146},
     doi = {10.18653/v1/2023.findings-emnlp.146},
     pages = {2210--2227},
     abstract = {Yes-no questions expect a yes or no for an answer, but people often skip polar keywords. Instead, they answer with long explanations that must be interpreted. In this paper, we focus on this challenging problem and release new benchmarks in eight languages. We present a distant supervision approach to collect training data, and demonstrate that direct answers (i.e., with polar keywords) are useful to train models to interpret indirect answers (i.e., without polar keywords). We show that monolingual fine-tuning is beneficial if training data can be obtained via distant supervision for the language of interest (5 languages). Additionally, we show that cross-lingual fine-tuning is always beneficial (8 languages).},
     month_numeric = {12}
   }
   

4. Portia Cooper, Mihai Surdeanu, and Eduardo Blanco. 2023. Hiding in Plain Sight: Tweets with Hate Speech Masked by Homoglyphs. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2922–2929, Singapore. Association for Computational Linguistics.

   @inproceedings{cooper-etal-2023-hiding,
     title = {Hiding in Plain Sight: Tweets with Hate Speech Masked by Homoglyphs},
     author = {Cooper, Portia and Surdeanu, Mihai and Blanco, Eduardo},
     editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
     month = dec,
     year = {2023},
     address = {Singapore},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.findings-emnlp.192},
     doi = {10.18653/v1/2023.findings-emnlp.192},
     pages = {2922--2929},
     abstract = {To avoid detection by current NLP monitoring applications, progenitors of hate speech often replace one or more letters in offensive words with homoglyphs, visually similar Unicode characters. Harvesting real-world hate speech containing homoglyphs is challenging due to the vast replacement possibilities. We developed a character substitution scraping method and assembled the Offensive Tweets with Homoglyphs (OTH) Dataset (N=90,788) with more than 1.5 million occurrences of 1,281 non-Latin characters (emojis excluded). In an annotated sample (n=700), 40.14{\%} of the tweets were found to contain hate speech. We assessed the performance of seven transformer-based hate speech detection models and found that they performed poorly in a zero-shot setting (F1 scores between 0.04 and 0.52) but normalizing the data dramatically improved detection (F1 scores between 0.59 and 0.71). Training the models using the annotated data further boosted performance (highest micro-averaged F1 score=0.88, using five-fold cross validation). This study indicates that a dataset containing homoglyphs known and unknown to the scraping script can be collected, and that neural models can be trained to recognize camouflaged real-world hate speech.},
     month_numeric = {12}
   }
   

5. Javad Asl, Eduardo Blanco, and Daniel Takabi. 2023. RobustEmbed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 4587–4603, Singapore. Association for Computational Linguistics.

   @inproceedings{asl-etal-2023-robustembed,
     title = {{R}obust{E}mbed: Robust Sentence Embeddings Using Self-Supervised Contrastive Pre-Training},
     author = {Asl, Javad and Blanco, Eduardo and Takabi, Daniel},
     editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
     month = dec,
     year = {2023},
     address = {Singapore},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.findings-emnlp.305},
     doi = {10.18653/v1/2023.findings-emnlp.305},
     pages = {4587--4603},
     abstract = {Pre-trained language models (PLMs) have demonstrated their exceptional performance across a wide range of natural language processing tasks. The utilization of PLM-based sentence embeddings enables the generation of contextual representations that capture rich semantic information. However, despite their success with unseen samples, current PLM-based representations suffer from poor robustness in adversarial scenarios. In this paper, we propose RobustEmbed, a self-supervised sentence embedding framework that enhances both generalization and robustness in various text representation tasks and against diverse adversarial attacks. By generating high-risk adversarial perturbations to promote higher invariance in the embedding space and leveraging the perturbation within a novel contrastive objective approach, RobustEmbed effectively learns high-quality sentence embeddings. Our extensive experiments validate the superiority of RobustEmbed over previous state-of-the-art self-supervised representations in adversarial settings, while also showcasing relative improvements in seven semantic textual similarity (STS) tasks and six transfer tasks. Specifically, our framework achieves a significant reduction in attack success rate from 75.51{\%} to 39.62{\%} for the BERTAttack attack technique, along with enhancements of 1.20{\%} and 0.40{\%} in STS tasks and transfer tasks, respectively.},
     month_numeric = {12}
   }
   

6. Shivam Mathur, Keun Park, Dhivya Chinnappa, Saketh Kotamraju, and Eduardo Blanco. 2023. Interpreting Answers to Yes-No Questions in User-Generated Content. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 14137–14161, Singapore. Association for Computational Linguistics.

   @inproceedings{mathur-etal-2023-interpreting,
     title = {Interpreting Answers to Yes-No Questions in User-Generated Content},
     author = {Mathur, Shivam and Park, Keun and Chinnappa, Dhivya and Kotamraju, Saketh and Blanco, Eduardo},
     editor = {Bouamor, Houda and Pino, Juan and Bali, Kalika},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2023},
     month = dec,
     year = {2023},
     address = {Singapore},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.findings-emnlp.942},
     doi = {10.18653/v1/2023.findings-emnlp.942},
     pages = {14137--14161},
     abstract = {Interpreting answers to yes-no questions in social media is difficult. Yes and no keywords are uncommon, and the few answers that include them are rarely to be interpreted what the keywords suggest. In this paper, we present a new corpus of 4,442 yes-no question-answer pairs from Twitter. We discuss linguistic characteristics of answers whose interpretation is yes or no, as well as answers whose interpretation is unknown. We show that large language models are far from solving this problem, even after fine-tuning and blending other corpora for the same problem but outside social media.},
     month_numeric = {12}
   }
   

7. Zhaomin Xiao, Yan Huang, and Eduardo Blanco. 2023. Context Helps Determine Spatial Knowledge from Tweets. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings), pages 149–160, Nusa Dua, Bali. Association for Computational Linguistics.

   @inproceedings{xiao-etal-2023-context,
     title = {Context Helps Determine Spatial Knowledge from Tweets},
     author = {Xiao, Zhaomin and Huang, Yan and Blanco, Eduardo},
     editor = {Park, Jong C. and Arase, Yuki and Hu, Baotian and Lu, Wei and Wijaya, Derry and Purwarianti, Ayu and Krisnadhi, Adila Alfa},
     booktitle = {Findings of the Association for Computational Linguistics: IJCNLP-AACL 2023 (Findings)},
     month = nov,
     year = {2023},
     address = {Nusa Dua, Bali},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.findings-ijcnlp.13},
     pages = {149--160},
     month_numeric = {11}
   }
   

8. Abdullah Albanyan, Ahmed Hassan, and Eduardo Blanco. 2023. Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis. In Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023), pages 71–88, Toronto, Canada. Association for Computational Linguistics.

   @inproceedings{albanyan-etal-2023-counterhate,
     title = {Not All Counterhate Tweets Elicit the Same Replies: A Fine-Grained Analysis},
     author = {Albanyan, Abdullah and Hassan, Ahmed and Blanco, Eduardo},
     booktitle = {Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023)},
     month = jul,
     year = {2023},
     address = {Toronto, Canada},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.starsem-1.8},
     doi = {10.18653/v1/2023.starsem-1.8},
     pages = {71--88},
     abstract = {Counterhate arguments can effectively fight and limit the spread of hate speech. However, they can also exacerbate the hate, as some people may respond with aggression if they feel threatened or targeted by the counterhate. In this paper, we investigate replies to counterhate arguments beyond whether the reply agrees or disagrees with the counterhate argument. We present a corpus with 2,621 replies to counterhate arguments countering hateful tweets, and annotate them with fine-grained characteristics. We show that (a) half of the replies (51{\%}) to the counterhate arguments disagree with the argument, and (b) this kind of reply often supports the hateful tweet (40{\%}). We also analyze the language of counterhate arguments that elicit certain types of replies. Experimental results show that it is feasible to anticipate the kind of replies a counterhate argument will elicit.},
     month_numeric = {7}
   }
   

9. Sushma Anand Akoju, Robert Vacareanu, Eduardo Blanco, Haris Riaz, and Mihai Surdeanu. 2023. Synthetic Dataset for Evaluating Complex Compositional Knowledge for Natural Language Inference. In Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE), pages 157–168, Toronto, Canada. Association for Computational Linguistics.

   @inproceedings{akoju-etal-2023-synthetic,
     title = {Synthetic Dataset for Evaluating Complex Compositional Knowledge for Natural Language Inference},
     author = {Akoju, Sushma Anand and Vacareanu, Robert and Blanco, Eduardo and Riaz, Haris and Surdeanu, Mihai},
     booktitle = {Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)},
     month = jun,
     year = {2023},
     address = {Toronto, Canada},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.nlrse-1.12},
     doi = {10.18653/v1/2023.nlrse-1.12},
     pages = {157--168},
     abstract = {We introduce a synthetic dataset called Sentences Involving Complex Compositional Knowledge (SICCK) and a novel analysis that investigates the performance of Natural Language Inference (NLI) models to understand compositionality in logic. We produce 1,304 sentence pairs by modifying 15 examples from the SICK dataset (Marelli et al., 2014). To this end, we modify the original texts using a set of phrases modifiers that correspond to universal quantifiers, existential quantifiers, negation, and other concept modifiers in Natural Logic (NL) (MacCartney, 2009). We use these phrases to modify the subject, verb, and object parts of the premise and hypothesis. Lastly, we annotate these modified texts with the corresponding entailment labels following NL rules. We conduct a preliminary verification of how well the change in the structural and semantic composition is captured by neural NLI models, in both zero-shot and fine-tuned scenarios. We found that the performance of NLI models under the zero-shot setting is poor, especially for modified sentences with negation and existential quantifiers. After fine-tuning this dataset, we observe that models continue to perform poorly over negation, existential and universal modifiers.},
     month_numeric = {6}
   }
   

2022

1. Md Mosharaf Hossain and Eduardo Blanco. 2022. Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5833–5847, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.

   @inproceedings{hossain-blanco-2022-leveraging,
     title = {Leveraging Affirmative Interpretations from Negation Improves Natural Language Understanding},
     author = {Hossain, Md Mosharaf and Blanco, Eduardo},
     booktitle = {Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing},
     month = dec,
     year = {2022},
     address = {Abu Dhabi, United Arab Emirates},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2022.emnlp-main.393},
     pages = {5833--5847},
     abstract = {Negation poses a challenge in many natural language understanding tasks. Inspired by the fact that understanding a negated statement often requires humans to infer affirmative interpretations, in this paper we show that doing so benefits models for three natural language understanding tasks. We present an automated procedure to collect pairs of sentences with negation and their affirmative interpretations, resulting in over 150,000 pairs. Experimental results show that leveraging these pairs helps (a) T5 generate affirmative interpretations from negations in a previous benchmark, and (b) a RoBERTa-based classifier solve the task of natural language inference. We also leverage our pairs to build a plug-and-play neural generator that given a negated statement generates an affirmative interpretation. Then, we incorporate the pretrained generator into a RoBERTa-based classifier for sentiment analysis and show that doing so improves the results. Crucially, our proposal does not require any manual effort.},
     month_numeric = {12}
   }
   

2. Zhaomin Xiao and Eduardo Blanco. 2022. Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2561–2571, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.

   @inproceedings{xiao-blanco-2022-people,
     title = {Are People Located in the Places They Mention in Their Tweets? A Multimodal Approach},
     author = {Xiao, Zhaomin and Blanco, Eduardo},
     booktitle = {Proceedings of the 29th International Conference on Computational Linguistics},
     month = oct,
     year = {2022},
     address = {Gyeongju, Republic of Korea},
     publisher = {International Committee on Computational Linguistics},
     url = {https://aclanthology.org/2022.coling-1.226},
     pages = {2561--2571},
     abstract = {This paper introduces the problem of determining whether people are located in the places they mention in their tweets. In particular, we investigate the role of text and images to solve this challenging problem. We present a new corpus of tweets that contain both text and images. Our analyses show that this problem is multimodal at its core: human judgments depend on whether annotators have access to the text, the image, or both. Experimental results show that a neural architecture that combines both modalities yields better results. We also conduct an error analysis to provide insights into why and when each modality is beneficial.},
     month_numeric = {10}
   }
   

3. Krishna Sanagavarapu, Jathin Singaraju, Anusha Kakileti, Anirudh Kaza, Aaron Mathews, Helen Li, Nathan Brito, and Eduardo Blanco. 2022. Disentangling Indirect Answers to Yes-No Questions in Real Conversations. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4677–4695, Seattle, United States. Association for Computational Linguistics.

   @inproceedings{sanagavarapu-etal-2022-disentangling,
     title = {Disentangling Indirect Answers to Yes-No Questions in Real Conversations},
     author = {Sanagavarapu, Krishna and Singaraju, Jathin and Kakileti, Anusha and Kaza, Anirudh and Mathews, Aaron and Li, Helen and Brito, Nathan and Blanco, Eduardo},
     booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
     month = jul,
     year = {2022},
     address = {Seattle, United States},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2022.naacl-main.345},
     pages = {4677--4695},
     abstract = {In this paper, we explore the task of determining indirect answers to yes-no questions in real conversations. We work with transcripts of phone conversations in the Switchboard Dialog Act (SwDA) corpus and create SwDA-IndirectAnswers (SwDA-IA), a subset of SwDA consisting of all conversations containing a yes-no question with an indirect answer. We annotate the underlying direct answers to the yes-no questions (yes, probably yes, middle, probably no, or no). We show that doing so requires taking into account conversation context: the indirect answer alone is insufficient to determine the ground truth. Experimental results also show that taking into account context is beneficial. More importantly, our results demonstrate that existing corpora with synthetic indirect answers to yes-no questions are not beneficial when working with real conversations. Our best models outperform the majority baseline by a substantial margin, but the task remains a challenge (F1: 0.46).},
     month_numeric = {7}
   }
   

4. Xinchen Yu, Eduardo Blanco, and Lingzi Hong. 2022. Hate Speech and Counter Speech Detection: Conversational Context Does Matter. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5918–5930, Seattle, United States. Association for Computational Linguistics.

   @inproceedings{yu-etal-2022-hate,
     title = {Hate Speech and Counter Speech Detection: Conversational Context Does Matter},
     author = {Yu, Xinchen and Blanco, Eduardo and Hong, Lingzi},
     booktitle = {Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
     month = jul,
     year = {2022},
     address = {Seattle, United States},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2022.naacl-main.433},
     pages = {5918--5930},
     abstract = {Hate speech is plaguing the cyberspace along with user-generated content. Adding counter speech has become an effective way to combat hate speech online. Existing datasets and models target either (a) hate speech or (b) hate and counter speech but disregard the context. This paper investigates the role of context in the annotation and detection of online hate and counter speech, where context is defined as the preceding comment in a conversation thread. We created a context-aware dataset for a 3-way classification task on Reddit comments: hate speech, counter speech, or neutral. Our analyses indicate that context is critical to identify hate and counter speech: human judgments change for most comments depending on whether we show annotators the context. A linguistic analysis draws insights into the language people use to express hate and counter speech. Experimental results show that neural networks obtain significantly better results if context is taken into account. We also present qualitative error analyses shedding light into (a) when and why context is beneficial and (b) the remaining errors made by our best model when context is taken into account.},
     month_numeric = {7}
   }
   

5. Md Mosharaf Hossain, Luke Holman, Anusha Kakileti, Tiffany Kao, Nathan Brito, Aaron Mathews, and Eduardo Blanco. 2022. A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations. In Findings of the Association for Computational Linguistics: NAACL 2022, pages 490–503, Seattle, United States. Association for Computational Linguistics.

   @inproceedings{hossain-etal-2022-question,
     title = {A Question-Answer Driven Approach to Reveal Affirmative Interpretations from Verbal Negations},
     author = {Hossain, Md Mosharaf and Holman, Luke and Kakileti, Anusha and Kao, Tiffany and Brito, Nathan and Mathews, Aaron and Blanco, Eduardo},
     booktitle = {Findings of the Association for Computational Linguistics: NAACL 2022},
     month = jul,
     year = {2022},
     address = {Seattle, United States},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2022.findings-naacl.37},
     pages = {490--503},
     abstract = {This paper explores a question-answer driven approach to reveal affirmative interpretations from verbal negations (i.e., when a negation cue grammatically modifies a verb). We create a new corpus consisting of 4,472 verbal negations and discover that 67.1{\%} of them convey that an event actually occurred. Annotators generate and answer 7,277 questions {\%} converted for 4,000 for the 3,001 negations that convey an affirmative interpretation. We first cast the problem of revealing affirmative interpretations from negations as a natural language inference (NLI) classification task. Experimental results show that state-of-the-art transformers trained with existing NLI corpora are insufficient to reveal affirmative interpretations. We also observe, however, that fine-tuning brings substantial improvements. In addition to NLI classification, we also explore the more realistic task of generating affirmative interpretations directly from negations with the T5 transformer. We conclude that the generation task remains a challenge as T5 substantially underperforms humans.},
     month_numeric = {7}
   }
   

6. Abdullah Albanyan and Eduardo Blanco. 2022. Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies. Proceedings of the AAAI Conference on Artificial Intelligence, 36(10):10418–10426.

   @article{Albanyan_Blanco_2022,
     title = {Pinpointing Fine-Grained Relationships between Hateful Tweets and Replies},
     volume = {36},
     url = {https://ojs.aaai.org/index.php/AAAI/article/view/21284},
     doi = {10.1609/aaai.v36i10.21284},
     abstractnote = {Recent studies in the hate and counter hate domain have provided the grounds for investigating how to detect this pervasive content in social media. These studies mostly work with synthetic replies to hateful content written by annotators on demand rather than replies written by real users. We argue that working with naturally occurring replies to hateful content is key to study the problem. Building on this motivation, we create a corpus of 5,652 hateful tweets and replies. We analyze their fine-grained relationships by indicating whether the reply (a) is hate or counter hate speech, (b) provides a justification, (c) attacks the author of the tweet, and (d) adds additional hate. We also present linguistic insights into the language people use depending on these fine-grained relationships. Experimental results show improvements (a) taking into account the hateful tweet in addition to the reply and (b) pretraining with related tasks.},
     number = {10},
     journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
     author = {Albanyan, Abdullah and Blanco, Eduardo},
     year = {2022},
     month = jun,
     pages = {10418-10426},
     month_numeric = {6}
   }
   

7. Md Mosharaf Hossain, Dhivya Chinnappa, and Eduardo Blanco. 2022. An Analysis of Negation in Natural Language Understanding Corpora. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 716–723, Dublin, Ireland. Association for Computational Linguistics.

   @inproceedings{hossain-etal-2022-analysis,
     title = {An Analysis of Negation in Natural Language Understanding Corpora},
     author = {Hossain, Md Mosharaf and Chinnappa, Dhivya and Blanco, Eduardo},
     booktitle = {Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
     month = may,
     year = {2022},
     address = {Dublin, Ireland},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2022.acl-short.81},
     doi = {10.18653/v1/2022.acl-short.81},
     pages = {716--723},
     abstract = {This paper analyzes negation in eight popular corpora spanning six natural language understanding tasks. We show that these corpora have few negations compared to general-purpose English, and that the few negations in them are often unimportant. Indeed, one can often ignore negations and still make the right predictions. Additionally, experimental results show that state-of-the-art transformers trained with these corpora obtain substantially worse results with instances that contain negation, especially if the negations are important. We conclude that new corpora accounting for negation are needed to solve natural language understanding tasks when negation is present.},
     month_numeric = {5}
   }
   

8. Jane E Hamilton, Eduardo Blanco, Salih Selek, Kelly L Wirfel, Elmer V Bernstam, Dawn Velligan, Meghana Gudala, and Kirk Roberts. 2022. Patient and Provider Perspectives on Medication Non-adherence Among Patients with Depression and/or Diabetes in Diverse Community Settings—A Qualitative Analysis. Patient Preference and Adherence, 16:1581–1594. Dove Press.

   @article{hamilton2022patient,
     title = {Patient and Provider Perspectives on Medication Non-adherence Among Patients with Depression and/or Diabetes in Diverse Community Settings---A Qualitative Analysis},
     author = {Hamilton, Jane E and Blanco, Eduardo and Selek, Salih and Wirfel, Kelly L and Bernstam, Elmer V and Velligan, Dawn and Gudala, Meghana and Roberts, Kirk},
     journal = {Patient Preference and Adherence},
     volume = {16},
     pages = {1581--1594},
     year = {2022},
     publisher = {Dove Press},
     doi = {10.2147/PPA.S328785}
   }
   

2021

1. Saketh Kotamraju and Eduardo Blanco. 2021. Written Justifications are Key to Aggregate Crowdsourced Forecasts. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4206–4216, Punta Cana, Dominican Republic. Association for Computational Linguistics.

   @inproceedings{kotamraju-blanco-2021-written-justifications,
     title = {Written Justifications are Key to Aggregate Crowdsourced Forecasts},
     author = {Kotamraju, Saketh and Blanco, Eduardo},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2021},
     month = nov,
     year = {2021},
     address = {Punta Cana, Dominican Republic},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2021.findings-emnlp.355},
     pages = {4206--4216},
     abstract = {This paper demonstrates that aggregating crowdsourced forecasts benefits from modeling the written justifications provided by forecasters. Our experiments show that the majority and weighted vote baselines are competitive, and that the written justifications are beneficial to call a question throughout its life except in the last quarter. We also conduct an error analysis shedding light into the characteristics that make a justification unreliable.},
     month_numeric = {11}
   }
   

2. Kaushik Madala, Shraddha Piparia, Eduardo Blanco, Hyunsook Do, and Renee Bryce. 2021. Model elements identification using neural networks: a comprehensive study. Requirements Engineering, 26:67–96. Springer.

   @article{madala2021model,
     title = {Model elements identification using neural networks: a comprehensive study},
     author = {Madala, Kaushik and Piparia, Shraddha and Blanco, Eduardo and Do, Hyunsook and Bryce, Renee},
     journal = {Requirements Engineering},
     volume = {26},
     pages = {67--96},
     year = {2021},
     publisher = {Springer},
     url = {https://link.springer.com/article/10.1007/s00766-020-00332-2}
   }
   

3. Dhivya Chinnappa and Eduardo Blanco. 2021. Extracting possessions from text: Experiments and error analysis. Natural Language Engineering:1–22. Cambridge University Press.

   @article{chinnappa_blanco_2021,
     title = {Extracting possessions from text: Experiments and error analysis},
     doi = {10.1017/S1351324921000012},
     journal = {Natural Language Engineering},
     publisher = {Cambridge University Press},
     author = {Chinnappa, Dhivya and Blanco, Eduardo},
     year = {2021},
     pages = {1–22}
   }
   

4. Eduardo Blanco and Roser Morante. 2021. Processing negation: An introduction to the special issue. Natural Language Engineering, 27(2):119–120. Cambridge University Press.

   @article{blanco_morante_2021,
     title = {Processing negation: An introduction to the special issue},
     volume = {27},
     doi = {10.1017/S1351324920000522},
     number = {2},
     journal = {Natural Language Engineering},
     publisher = {Cambridge University Press},
     author = {Blanco, Eduardo and Morante, Roser},
     year = {2021},
     pages = {119–120}
   }
   

5. Paul Tarau and Eduardo Blanco. 2021. Interactive Text Graph Mining with a Prolog-Based Dialog Engine. Theory and Practice of Logic Programming, 21(2):244–263. Cambridge University Press.

   @article{tarau_blanco_2021,
     title = {Interactive Text Graph Mining with a Prolog-Based Dialog Engine},
     volume = {21},
     doi = {10.1017/S1471068420000137},
     number = {2},
     journal = {Theory and Practice of Logic Programming},
     publisher = {Cambridge University Press},
     author = {Tarau, Paul and Blanco, Eduardo},
     year = {2021},
     pages = {244–263}
   }
   

6. Roser Morante and Eduardo Blanco. 2021. Recent advances in processing negation. Natural Language Engineering, 27(2):121–130. Cambridge University Press.

   @article{morante_blanco_2021,
     title = {Recent advances in processing negation},
     volume = {27},
     doi = {10.1017/S1351324920000534},
     number = {2},
     journal = {Natural Language Engineering},
     publisher = {Cambridge University Press},
     author = {Morante, Roser and Blanco, Eduardo},
     year = {2021},
     pages = {121–130}
   }
   

7. Alakananda Vempala and Eduardo Blanco. 2021. Temporally anchored spatial knowledge: Corpora and experiments. Natural Language Engineering, 27(5):519–543. Cambridge University Press.

   @article{vempala_blanco_2021,
     title = {Temporally anchored spatial knowledge: Corpora and experiments},
     volume = {27},
     doi = {10.1017/S1351324920000212},
     number = {5},
     journal = {Natural Language Engineering},
     publisher = {Cambridge University Press},
     author = {Vempala, Alakananda and Blanco, Eduardo},
     year = {2021},
     pages = {519–543}
   }
   

2020

1. Jordan Sanders, Meghana Gudala, Kathleen Hamilton, Nishtha Prasad, Jordan Stovall, Eduardo Blanco, Jane E Hamilton, and Kirk Roberts. 2020. Extracting Adherence Information from Electronic Health Records. In Proceedings of the 28th International Conference on Computational Linguistics, pages 680–695, Barcelona, Spain (Online). International Committee on Computational Linguistics.

   @inproceedings{sanders-etal-2020-extracting,
     title = {Extracting Adherence Information from Electronic Health Records},
     author = {Sanders, Jordan and Gudala, Meghana and Hamilton, Kathleen and Prasad, Nishtha and Stovall, Jordan and Blanco, Eduardo and Hamilton, Jane E and Roberts, Kirk},
     booktitle = {Proceedings of the 28th International Conference on Computational Linguistics},
     month = dec,
     year = {2020},
     address = {Barcelona, Spain (Online)},
     publisher = {International Committee on Computational Linguistics},
     url = {https://aclanthology.org/2020.coling-main.60},
     doi = {10.18653/v1/2020.coling-main.60},
     pages = {680--695},
     abstract = {Patient adherence is a critical factor in health outcomes. We present a framework to extract adherence information from electronic health records, including both sentence-level information indicating general adherence information (full, partial, none, etc.) and span-level information providing additional information such as adherence type (medication or nonmedication), reasons and outcomes. We annotate and make publicly available a new corpus of 3,000 de-identified sentences, and discuss the language physicians use to document adherence information. We also explore models based on state-of-the-art transformers to automate both tasks.},
     month_numeric = {12}
   }
   

2. Md Mosharaf Hossain, Venelin Kovatchev, Pranoy Dutta, Tiffany Kao, Elizabeth Wei, and Eduardo Blanco. 2020. An Analysis of Natural Language Inference Benchmarks through the Lens of Negation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 9106–9118, Online. Association for Computational Linguistics.

   @inproceedings{hossain-etal-2020-analysis,
     title = {An Analysis of Natural Language Inference Benchmarks through the Lens of Negation},
     author = {Hossain, Md Mosharaf and Kovatchev, Venelin and Dutta, Pranoy and Kao, Tiffany and Wei, Elizabeth and Blanco, Eduardo},
     booktitle = {Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
     month = nov,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2020.emnlp-main.732},
     doi = {10.18653/v1/2020.emnlp-main.732},
     pages = {9106--9118},
     abstract = {Negation is underrepresented in existing natural language inference benchmarks. Additionally, one can often ignore the few negations in existing benchmarks and still make the right inference judgments. In this paper, we present a new benchmark for natural language inference in which negation plays a critical role. We also show that state-of-the-art transformers struggle making inference judgments with the new pairs.},
     month_numeric = {11}
   }
   

3. Farzana Rashid, Tommaso Fornaciari, Dirk Hovy, Eduardo Blanco, and Fernando Vega-Redondo. 2020. Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2366–2371, Online. Association for Computational Linguistics.

   @inproceedings{rashid-etal-2020-helpful,
     title = {Helpful or Hierarchical? Predicting the Communicative Strategies of Chat Participants, and their Impact on Success},
     author = {Rashid, Farzana and Fornaciari, Tommaso and Hovy, Dirk and Blanco, Eduardo and Vega-Redondo, Fernando},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
     month = nov,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2020.findings-emnlp.214},
     doi = {10.18653/v1/2020.findings-emnlp.214},
     pages = {2366--2371},
     abstract = {When interacting with each other, we motivate, advise, inform, show love or power towards our peers. However, the way we interact may also hold some indication on how successful we are, as people often try to help each other to achieve their goals. We study the chat interactions of thousands of aspiring entrepreneurs who discuss and develop business models. We manually annotate a set of about 5,500 chat interactions with four dimensions of interaction styles (motivation, cooperation, equality, advice). We find that these styles can be reliably predicted, and that the communication styles can be used to predict a number of indices of business success. Our findings indicate that successful communicators are also successful in other domains.},
     month_numeric = {11}
   }
   

4. Md Mosharaf Hossain, Antonios Anastasopoulos, Eduardo Blanco, and Alexis Palmer. 2020. It’s not a Non-Issue: Negation as a Source of Error in Machine Translation. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 3869–3885, Online. Association for Computational Linguistics.

   @inproceedings{hossain-etal-2020-non,
     title = {It{'}s not a Non-Issue: Negation as a Source of Error in Machine Translation},
     author = {Hossain, Md Mosharaf and Anastasopoulos, Antonios and Blanco, Eduardo and Palmer, Alexis},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
     month = nov,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2020.findings-emnlp.345},
     doi = {10.18653/v1/2020.findings-emnlp.345},
     pages = {3869--3885},
     abstract = {As machine translation (MT) systems progress at a rapid pace, questions of their adequacy linger. In this study we focus on negation, a universal, core property of human language that significantly affects the semantics of an utterance. We investigate whether translating negation is an issue for modern MT systems using 17 translation directions as test bed. Through thorough analysis, we find that indeed the presence of negation can significantly impact downstream quality, in some cases resulting in quality reductions of more than 60{\%}. We also provide a linguistically motivated analysis that directly explains the majority of our findings. We release our annotations and code to replicate our analysis here: https://github.com/mosharafhossain/negation-mt.},
     month_numeric = {11}
   }
   

5. Srikala Murugan, Dhivya Chinnappa, and Eduardo Blanco. 2020. Determining Event Outcomes: The Case of #fail. In Findings of the Association for Computational Linguistics: EMNLP 2020, pages 4021–4033, Online. Association for Computational Linguistics.

   @inproceedings{murugan-etal-2020-determining,
     title = {Determining Event Outcomes: The Case of {\#}fail},
     author = {Murugan, Srikala and Chinnappa, Dhivya and Blanco, Eduardo},
     booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2020},
     month = nov,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2020.findings-emnlp.359},
     doi = {10.18653/v1/2020.findings-emnlp.359},
     pages = {4021--4033},
     abstract = {This paper targets the task of determining event outcomes in social media. We work with tweets containing either {\#}cookingFail or {\#}bakingFail, and show that many of the events described in them resulted in something edible. Tweets that contain images are more likely to result in edible albeit imperfect outcomes. Experimental results show that edibility is easier to predict than outcome quality.},
     month_numeric = {11}
   }
   

6. Dhivya Chinnappa, Srikala Murugan, and Eduardo Blanco. 2020. Beyond Possession Existence: Duration and Co-Possession. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8332–8341, Online. Association for Computational Linguistics.

   @inproceedings{chinnappa-etal-2020-beyond,
     title = {Beyond Possession Existence: Duration and Co-Possession},
     author = {Chinnappa, Dhivya and Murugan, Srikala and Blanco, Eduardo},
     booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
     month = jul,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2020.acl-main.739},
     doi = {10.18653/v1/2020.acl-main.739},
     pages = {8332--8341},
     abstract = {This paper introduces two tasks: determining (a) the duration of possession relations and (b) co-possessions, i.e., whether multiple possessors possess a possessee at the same time. We present new annotations on top of corpora annotating possession existence and experimental results. Regarding possession duration, we derive the time spans we work with empirically from annotations indicating lower and upper bounds. Regarding co-possessions, we use a binary label. Cohen{'}s kappa coefficients indicate substantial agreement, and experimental results show that text is more useful than the image for solving these tasks.},
     month_numeric = {7}
   }
   

7. Md Mosharaf Hossain, Kathleen Hamilton, Alexis Palmer, and Eduardo Blanco. 2020. Predicting the Focus of Negation: Model and Error Analysis. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8389–8401, Online. Association for Computational Linguistics.

   @inproceedings{hossain-etal-2020-predicting,
     title = {Predicting the Focus of Negation: Model and Error Analysis},
     author = {Hossain, Md Mosharaf and Hamilton, Kathleen and Palmer, Alexis and Blanco, Eduardo},
     booktitle = {Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics},
     month = jul,
     year = {2020},
     address = {Online},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2020.acl-main.743},
     doi = {10.18653/v1/2020.acl-main.743},
     pages = {8389--8401},
     abstract = {The focus of a negation is the set of tokens intended to be negated, and a key component for revealing affirmative alternatives to negated utterances. In this paper, we experiment with neural networks to predict the focus of negation. Our main novelty is leveraging a scope detector to introduce the scope of negation as an additional input to the network. Experimental results show that doing so obtains the best results to date. Additionally, we perform a detailed error analysis providing insights into the main error categories, and analyze errors depending on whether the model takes into account scope and context information.},
     month_numeric = {7}
   }
   

8. Jiménez-Zafra Salud Marı́a, Roser Morante, Eduardo Blanco, Martı́n Valdivia Marı́a Teresa, and L. Alfonso Ureña López. 2020. Detecting Negation Cues and Scopes in Spanish. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 6902–6911, Marseille, France. European Language Resources Association.

   @inproceedings{jimenez-zafra-etal-2020-detecting,
     title = {Detecting Negation Cues and Scopes in {S}panish},
     author = {Jim{\'e}nez-Zafra, Salud Mar{\'\i}a and Morante, Roser and Blanco, Eduardo and Mart{\'\i}n Valdivia, Mar{\'\i}a Teresa and Ure{\~n}a L{\'o}pez, L. Alfonso},
     booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
     month = may,
     year = {2020},
     address = {Marseille, France},
     publisher = {European Language Resources Association},
     url = {https://aclanthology.org/2020.lrec-1.853},
     pages = {6902--6911},
     abstract = {In this work we address the processing of negation in Spanish. We first present a machine learning system that processes negation in Spanish. Specifically, we focus on two tasks: i) negation cue detection and ii) scope identification. The corpus used in the experimental framework is the SFU Corpus. The results for cue detection outperform state-of-the-art results, whereas for scope detection this is the first system that performs the task for Spanish. Moreover, we provide a qualitative error analysis aimed at understanding the limitations of the system and showing which negation cues and scopes are straightforward to predict automatically, and which ones are challenging.},
     language = {English},
     isbn = {979-10-95546-34-4},
     month_numeric = {5}
   }
   

9. Dhivya Chinnappa, Alexis Palmer, and Eduardo Blanco. 2020. WikiPossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 1110–1117, Marseille, France. European Language Resources Association.

   @inproceedings{chinnappa-etal-2020-wikipossessions,
     title = {{W}iki{P}ossessions: Possession Timeline Generation as an Evaluation Benchmark for Machine Reading Comprehension of Long Texts},
     author = {Chinnappa, Dhivya and Palmer, Alexis and Blanco, Eduardo},
     booktitle = {Proceedings of the 12th Language Resources and Evaluation Conference},
     month = may,
     year = {2020},
     address = {Marseille, France},
     publisher = {European Language Resources Association},
     url = {https://aclanthology.org/2020.lrec-1.140},
     pages = {1110--1117},
     abstract = {This paper presents WikiPossessions, a new benchmark corpus for the task of temporally-oriented possession (TOP), or tracking objects as they change hands over time. We annotate Wikipedia articles for 90 different well-known artifacts paintings, diamonds, and archaeological artifacts), producing 799 artifact-possessor relations with associated attributes. For each article, we also produce a full possession timeline. The full version of the task combines straightforward entity-relation extraction with complex temporal reasoning, as well as verification of textual support for the relevant types of knowledge. Specifically, to complete the full TOP task for a given article, a system must do the following: a) identify possessors; b) anchor possessors to times/events; c) identify temporal relations between each temporal anchor and the possession relation it corresponds to; d) assign certainty scores to each possessor and each temporal relation; and e) assemble individual possession events into a global possession timeline. In addition to the corpus, we release evaluation scripts and a baseline model for the task.},
     language = {English},
     isbn = {979-10-95546-34-4},
     month_numeric = {5}
   }
   

10. Paul Tarau and Eduardo Blanco. 2020. Interactive Text Graph Mining with a Prolog-based Dialog Engine. In Practical Aspects of Declarative Languages, pages 3–19. Springer International Publishing.

    @inproceedings{10.1007/978-3-030-39197-3_1,
      author = {Tarau, Paul and Blanco, Eduardo},
      title = {Interactive Text Graph Mining with a Prolog-based Dialog Engine},
      booktitle = {Practical Aspects of Declarative Languages},
      year = {2020},
      publisher = {Springer International Publishing},
      pages = {3--19},
      abstract = {On top of a neural network-based dependency parser and a graph-based natural language processing module we design a Prolog-based dialog engine that explores interactively a ranked fact database extracted from a text document.},
      isbn = {978-3-030-39197-3},
      url = {https://link.springer.com/chapter/10.1007/978-3-030-39197-3_1}
    }
    

11. Alakananda Vempala and Eduardo Blanco. 2020. Extracting Biographical Spatial Timelines: Corpus and Experiments. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 28:1395–1403.

    @article{9072395,
      author = {Vempala, Alakananda and Blanco, Eduardo},
      journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
      title = {Extracting Biographical Spatial Timelines: Corpus and Experiments},
      year = {2020},
      volume = {28},
      number = {},
      pages = {1395-1403},
      doi = {10.1109/TASLP.2020.2988418}
    }
    

2019

1. Manar Alohaly, Hassan Takabi, and Eduardo Blanco. 2019. Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies. Cybersecurity, 2(1). Springer Open.

   @article{9e535f157c984e168757dd4fe2b7e289,
     title = {Automated extraction of attributes from natural language attribute-based access control (ABAC) Policies},
     abstract = {The National Institute of Standards and Technology (NIST) has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy (NLACP) to a machine-readable form. To study the automation process, we consider the hierarchical ABAC model as our reference model since it better reflects the requirements of real-world organizations. Therefore, this paper focuses on the questions of: how can we automatically infer the hierarchical structure of an ABAC model given NLACPs; and, how can we extract and define the set of authorization attributes based on the resulting structure. To address these questions, we propose an approach built upon recent advancements in natural language processing and machine learning techniques. For such a solution, the lack of appropriate data often poses a bottleneck. Therefore, we decouple the primary contributions of this work into: (1) developing a practical framework to extract authorization attributes of hierarchical ABAC system from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework. Our experimental results are promising as we achieved - in average - an F1-score of 0.96 when extracting attributes values of subjects, and 0.91 when extracting the values of objects{\textquoteright} attributes from natural language access control policies.},
     keywords = {Attribute-based access control (ABAC) policy authoring natural language processing relation extraction clustering deep learning},
     author = {Alohaly, Manar and Takabi, Hassan and Blanco, Eduardo},
     year = {2019},
     month = dec,
     day = {1},
     doi = {10.1186/s42400-018-0019-2},
     language = {English (US)},
     volume = {2},
     journal = {Cybersecurity},
     issn = {2096-4862},
     publisher = {Springer Open},
     number = {1},
     month_numeric = {12}
   }
   

2. Dhivya Chinnappa, Srikala Murugan, and Eduardo Blanco. 2019. Extracting Possessions from Social Media: Images Complement Language. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 663–672, Hong Kong, China. Association for Computational Linguistics.

   @inproceedings{chinnappa-etal-2019-extracting,
     title = {Extracting Possessions from Social Media: Images Complement Language},
     author = {Chinnappa, Dhivya and Murugan, Srikala and Blanco, Eduardo},
     booktitle = {Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)},
     month = nov,
     year = {2019},
     address = {Hong Kong, China},
     publisher = {Association for Computational Linguistics},
     url = {https://www.aclweb.org/anthology/D19-1061},
     doi = {10.18653/v1/D19-1061},
     pages = {663--672},
     abstract = {This paper describes a new dataset and experiments to determine whether authors of tweets possess the objects they tweet about. We work with 5,000 tweets and show that both humans and neural networks benefit from images in addition to text. We also introduce a simple yet effective strategy to incorporate visual information into any neural network beyond weights from pretrained networks. Specifically, we consider the tags identified in an image as an additional textual input, and leverage pretrained word embeddings as usually done with regular text. Experimental results show this novel strategy is beneficial.},
     month_numeric = {11}
   }
   

3. Zahra Sarabi, Erin Killian, Eduardo Blanco, and Alexis Palmer. 2019. A Corpus of Negations and their Underlying Positive Interpretations. In Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019), pages 158–167, Minneapolis, Minnesota. Association for Computational Linguistics.

   @inproceedings{sarabi-etal-2019-corpus,
     title = {A Corpus of Negations and their Underlying Positive Interpretations},
     author = {Sarabi, Zahra and Killian, Erin and Blanco, Eduardo and Palmer, Alexis},
     booktitle = {Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*{SEM} 2019)},
     month = jun,
     year = {2019},
     address = {Minneapolis, Minnesota},
     publisher = {Association for Computational Linguistics},
     url = {https://www.aclweb.org/anthology/S19-1017},
     pages = {158--167},
     abstract = {Negation often conveys implicit positive meaning. In this paper, we present a corpus of negations and their underlying positive interpretations. We work with negations from Simple Wikipedia, automatically generate potential positive interpretations, and then collect manual annotations that effectively rewrite the negation in positive terms. This procedure yields positive interpretations for approximately 77{\%} of negations, and the final corpus includes over 5,700 negations and over 5,900 positive interpretations. We also present baseline results using seq2seq neural models.},
     month_numeric = {6}
   }
   

4. Abhishek Singh, Eduardo Blanco, and Wei Jin. 2019. Incorporating Emoji Descriptions Improves Tweet Classification. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 2096–2101, Minneapolis, Minnesota. Association for Computational Linguistics.

   @inproceedings{singh-etal-2019-incorporating,
     title = {Incorporating Emoji Descriptions Improves Tweet Classification},
     author = {Singh, Abhishek and Blanco, Eduardo and Jin, Wei},
     booktitle = {Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)},
     month = jun,
     year = {2019},
     address = {Minneapolis, Minnesota},
     publisher = {Association for Computational Linguistics},
     url = {https://www.aclweb.org/anthology/N19-1214},
     pages = {2096--2101},
     abstract = {Tweets are short messages that often include specialized language such as hashtags and emojis. In this paper, we present a simple strategy to process emojis: replace them with their natural language description and use pretrained word embeddings as normally done with standard words. We show that this strategy is more effective than using pretrained emoji embeddings for tweet classification. Specifically, we obtain new state-of-the-art results in irony detection and sentiment analysis despite our neural network is simpler than previous proposals.},
     month_numeric = {6}
   }
   

5. Zahra Sarabi and Eduardo Blanco. 2019. Identifying the Focus of Negation Using Discourse Structure. In Proceedings of the Thirty-Second International Florida Artificial Intelligence Research Society Conference (FLAIRS), pages 448–452.

   @inproceedings{DBLP:conf/flairs/Sarabi019,
     author = {Sarabi, Zahra and Blanco, Eduardo},
     title = {Identifying the Focus of Negation Using Discourse Structure},
     booktitle = {Proceedings of the Thirty-Second International Florida Artificial
                    Intelligence Research Society Conference (FLAIRS)},
     location = {Sarasota, Florida},
     pages = {448--452},
     year = {2019},
     url = {https://aaai.org/ocs/index.php/FLAIRS/FLAIRS19/paper/view/18145}
   }
   

6. Manar Alohaly, Hassan Takabi, and Eduardo Blanco. 2019. Towards an Automated Extraction of ABAC Constraints from Natural Language Policies. In IFIP International Conference on ICT Systems Security and Privacy Protection, pages 105–119. Springer.

   @inproceedings{alohaly2019towards,
     title = {Towards an Automated Extraction of ABAC Constraints from Natural Language Policies},
     author = {Alohaly, Manar and Takabi, Hassan and Blanco, Eduardo},
     booktitle = {IFIP International Conference on ICT Systems Security and Privacy Protection},
     pages = {105--119},
     year = {2019},
     organization = {Springer},
     url = {https://www.springerprofessional.de/en/towards-an-automated-extraction-of-abac-constraints-from-natural/16817308}
   }
   

2018

1. Dhivya Chinnappa and Eduardo Blanco. 2018. Possessors Change Over Time: A Case Study with Artworks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2278–2287, Brussels, Belgium. Association for Computational Linguistics.

   @inproceedings{chinnappa-blanco-2018-possessors,
     title = {Possessors Change Over Time: A Case Study with Artworks},
     author = {Chinnappa, Dhivya and Blanco, Eduardo},
     booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
     month = oct,
     year = {2018},
     address = {Brussels, Belgium},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/D18-1251},
     doi = {10.18653/v1/D18-1251},
     pages = {2278--2287},
     abstract = {This paper presents a corpus and experimental results to extract possession relations over time. We work with Wikipedia articles about artworks, and extract possession relations along with temporal information indicating when these relations are true. The annotation scheme yields many possessors over time for a given artwork, and experimental results show that an LSTM ensemble can automate the task.},
     month_numeric = {10}
   }
   

2. Farzana Rashid and Eduardo Blanco. 2018. Characterizing Interactions and Relationships between People. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4395–4404, Brussels, Belgium. Association for Computational Linguistics.

   @inproceedings{rashid-blanco-2018-characterizing,
     title = {Characterizing Interactions and Relationships between People},
     author = {Rashid, Farzana and Blanco, Eduardo},
     booktitle = {Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
     month = oct,
     year = {2018},
     address = {Brussels, Belgium},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/D18-1470},
     doi = {10.18653/v1/D18-1470},
     pages = {4395--4404},
     abstract = {This paper presents a set of dimensions to characterize the association between two people. We distinguish between interactions (when somebody refers to somebody in a conversation) and relationships (a sequence of interactions). We work with dialogue scripts from the TV show Friends, and do not impose any restrictions on the interactions and relationships. We introduce and analyze a new corpus, and present experimental results showing that the task can be automated.},
     month_numeric = {10}
   }
   

3. Alakananda Vempala, Eduardo Blanco, and Alexis Palmer. 2018. Determining Event Durations: Models and Error Analysis. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 164–168, New Orleans, Louisiana. Association for Computational Linguistics.

   @inproceedings{vempala-etal-2018-determining,
     title = {Determining Event Durations: Models and Error Analysis},
     author = {Vempala, Alakananda and Blanco, Eduardo and Palmer, Alexis},
     booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)},
     month = jun,
     year = {2018},
     address = {New Orleans, Louisiana},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/N18-2026},
     doi = {10.18653/v1/N18-2026},
     pages = {164--168},
     abstract = {This paper presents models to predict event durations. We introduce aspectual features that capture deeper linguistic information than previous work, and experiment with neural networks. Our analysis shows that tense, aspect and temporal structure of the clause provide useful clues, and that an LSTM ensemble captures relevant context around the event.},
     month_numeric = {6}
   }
   

4. Dhivya Chinnappa and Eduardo Blanco. 2018. Mining Possessions: Existence, Type and Temporal Anchors. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 496–505, New Orleans, Louisiana. Association for Computational Linguistics.

   @inproceedings{chinnappa-blanco-2018-mining,
     title = {Mining Possessions: Existence, Type and Temporal Anchors},
     author = {Chinnappa, Dhivya and Blanco, Eduardo},
     booktitle = {Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)},
     month = jun,
     year = {2018},
     address = {New Orleans, Louisiana},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/N18-1046},
     doi = {10.18653/v1/N18-1046},
     pages = {496--505},
     abstract = {This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions, and assign temporal anchors indicating when the possession holds between possessor and possessee. We present new annotations for this task, and experimental results using both traditional classifiers and neural networks. Results show that the three subtasks (predicting possession existence, possession type and temporal anchors) can be automated.},
     month_numeric = {6}
   }
   

5. Vivek Reddy Doudagiri, Alakananda Vempala, and Eduardo Blanco. 2018. Annotating If the Authors of a Tweet are Located at the Locations They Tweet About. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).

   @inproceedings{DOUDAGIRI18.1011,
     author = {Doudagiri, Vivek Reddy and Vempala, Alakananda and Blanco, Eduardo},
     title = {{Annotating If the Authors of a Tweet are Located at the Locations They Tweet About}},
     booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
     year = {2018},
     month = may,
     address = {Miyazaki, Japan},
     editor = {chair), Nicoletta Calzolari (Conference and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, Hélène and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu},
     publisher = {European Language Resources Association (ELRA)},
     isbn = {979-10-95546-00-9},
     language = {english},
     url = {https://aclanthology.org/L18-1280/},
     month_numeric = {5}
   }
   

6. Alakananda Vempala and Eduardo Blanco. 2018. Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies. In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan. European Language Resources Association (ELRA).

   @inproceedings{VEMPALA18.951,
     author = {Vempala, Alakananda and Blanco, Eduardo},
     title = {{Annotating Temporally-Anchored Spatial Knowledge by Leveraging Syntactic Dependencies}},
     booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)},
     year = {2018},
     month = may,
     address = {Miyazaki, Japan},
     editor = {chair), Nicoletta Calzolari (Conference and Choukri, Khalid and Cieri, Christopher and Declerck, Thierry and Goggi, Sara and Hasida, Koiti and Isahara, Hitoshi and Maegaard, Bente and Mariani, Joseph and Mazo, Hélène and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios and Tokunaga, Takenobu},
     publisher = {European Language Resources Association (ELRA)},
     isbn = {979-10-95546-00-9},
     language = {english},
     url = {https://aclanthology.org/L18-1052/},
     month_numeric = {5}
   }
   

7. Manar Alohaly, Hassan Takabi, and Eduardo Blanco. 2018. A Deep Learning Approach for Extracting Attributes of ABAC Policies. In Proceedings of the 23nd ACM on Symposium on Access Control Models and Technologies, pages 137–148, New York, NY, USA. Association for Computing Machinery.

   @inproceedings{10.1145/3205977.3205984,
     author = {Alohaly, Manar and Takabi, Hassan and Blanco, Eduardo},
     title = {A Deep Learning Approach for Extracting Attributes of ABAC Policies},
     year = {2018},
     isbn = {9781450356664},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     url = {https://doi.org/10.1145/3205977.3205984},
     doi = {10.1145/3205977.3205984},
     abstract = {The National Institute of Standards and Technology (NIST) has identified natural language policies as the preferred expression of policy and implicitly called for an automated translation of ABAC natural language access control policy (NLACP) to a machine-readable form. An essential step towards this automation is to automate the extraction of ABAC attributes from NLACPs, which is the focus of this paper. We, therefore, raise the question of: how can we automate the task of attributes extraction from natural language documents? Our proposed solution to this question is built upon the recent advancements in natural language processing and machine learning techniques. For such a solution, the lack of appropriate data often poses a bottleneck. Therefore, we decouple the primary contributions of this work into: (1) developing a practical framework to extract ABAC attributes from natural language artifacts, and (2) generating a set of realistic synthetic natural language access control policies (NLACPs) to evaluate the proposed framework. The experimental results are promising with regard to the potential automation of the task of interest. Using a convolutional neural network (CNN), we achieved - in average - an F1-score of 0.96 when extracting the attributes of subjects, and 0.91 when extracting the objects' attributes from natural language access control policies.},
     booktitle = {Proceedings of the 23nd ACM on Symposium on Access Control Models and Technologies},
     pages = {137–148},
     numpages = {12},
     keywords = {access control policy, relation extraction, deep learning, attribute-based access control, policy authoring, natural language processing},
     location = {Indianapolis, Indiana, USA},
     series = {SACMAT '18}
   }
   

2017

1. Farzana Rashid and Eduardo Blanco. 2017. Dimensions of Interpersonal Relationships: Corpus and Experiments. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2307–2316, Copenhagen, Denmark. Association for Computational Linguistics.

   @inproceedings{rashid-blanco-2017-dimensions,
     title = {Dimensions of Interpersonal Relationships: Corpus and Experiments},
     author = {Rashid, Farzana and Blanco, Eduardo},
     booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
     month = sep,
     year = {2017},
     address = {Copenhagen, Denmark},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/D17-1244},
     doi = {10.18653/v1/D17-1244},
     pages = {2307--2316},
     abstract = {This paper presents a corpus and experiments to determine dimensions of interpersonal relationships. We define a set of dimensions heavily inspired by work in social science. We create a corpus by retrieving pairs of people, and then annotating dimensions for their relationships. A corpus analysis shows that dimensions can be annotated reliably. Experimental results show that given a pair of people, values to dimensions can be assigned automatically.},
     month_numeric = {9}
   }
   

2. Krishna Chaitanya Sanagavarapu, Alakananda Vempala, and Eduardo Blanco. 2017. Determining Whether and When People Participate in the Events They Tweet About. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 641–646, Vancouver, Canada. Association for Computational Linguistics.

   @inproceedings{sanagavarapu-etal-2017-determining,
     title = {Determining Whether and When People Participate in the Events They Tweet About},
     author = {Sanagavarapu, Krishna Chaitanya and Vempala, Alakananda and Blanco, Eduardo},
     booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)},
     month = jul,
     year = {2017},
     address = {Vancouver, Canada},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/P17-2101},
     doi = {10.18653/v1/P17-2101},
     pages = {641--646},
     abstract = {This paper describes an approach to determine whether people participate in the events they tweet about. Specifically, we determine whether people are participants in events with respect to the tweet timestamp. We target all events expressed by verbs in tweets, including past, present and events that may occur in the future. We present new annotations using 1,096 event mentions, and experimental results showing that the task is challenging.},
     month_numeric = {7}
   }
   

3. Zahra Sarabi and Eduardo Blanco. 2017. If No Media Were Allowed inside the Venue, Was Anybody Allowed? In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 860–869, Valencia, Spain. Association for Computational Linguistics.

   @inproceedings{sarabi-blanco-2017-media,
     title = {If No Media Were Allowed inside the Venue, Was Anybody Allowed?},
     author = {Sarabi, Zahra and Blanco, Eduardo},
     booktitle = {Proceedings of the 15th Conference of the {E}uropean Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
     month = apr,
     year = {2017},
     address = {Valencia, Spain},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/E17-1081},
     pages = {860--869},
     abstract = {This paper presents a framework to understand negation in positive terms. Specifically, we extract positive meaning from negation when the negation cue syntactically modifies a noun or adjective. Our approach is grounded on generating potential positive interpretations automatically, and then scoring them. Experimental results show that interpretations scored high can be reliably identified.},
     month_numeric = {4}
   }
   

2016

1. Zahra Sarabi and Eduardo Blanco. 2016. Understanding Negation in Positive Terms Using Syntactic Dependencies. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1108–1118, Austin, Texas. Association for Computational Linguistics.

   @inproceedings{sarabi-blanco-2016-understanding,
     title = {Understanding Negation in Positive Terms Using Syntactic Dependencies},
     author = {Sarabi, Zahra and Blanco, Eduardo},
     booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
     month = nov,
     year = {2016},
     address = {Austin, Texas},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/D16-1119},
     doi = {10.18653/v1/D16-1119},
     pages = {1108--1118},
     month_numeric = {11}
   }
   

2. Jordan Sanders and Eduardo Blanco. 2016. Automatic Extraction of Implicit Interpretations from Modal Constructions. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 1098–1107, Austin, Texas. Association for Computational Linguistics.

   @inproceedings{sanders-blanco-2016-automatic,
     title = {Automatic Extraction of Implicit Interpretations from Modal Constructions},
     author = {Sanders, Jordan and Blanco, Eduardo},
     booktitle = {Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing},
     month = nov,
     year = {2016},
     address = {Austin, Texas},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/D16-1118},
     doi = {10.18653/v1/D16-1118},
     pages = {1098--1107},
     month_numeric = {11}
   }
   

3. Alakananda Vempala and Eduardo Blanco. 2016. Beyond Plain Spatial Knowledge: Determining Where Entities Are and Are Not Located, and For How Long. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1502–1512, Berlin, Germany. Association for Computational Linguistics.

   @inproceedings{vempala-blanco-2016-beyond,
     title = {Beyond Plain Spatial Knowledge: Determining Where Entities Are and Are Not Located, and For How Long},
     author = {Vempala, Alakananda and Blanco, Eduardo},
     booktitle = {Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
     month = aug,
     year = {2016},
     address = {Berlin, Germany},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/P16-1142},
     doi = {10.18653/v1/P16-1142},
     pages = {1502--1512},
     month_numeric = {8}
   }
   

4. Eduardo Blanco and Zahra Sarabi. 2016. Automatic Generation and Scoring of Positive Interpretations from Negated Statements. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1431–1441, San Diego, California. Association for Computational Linguistics.

   @inproceedings{blanco-sarabi-2016-automatic,
     title = {Automatic Generation and Scoring of Positive Interpretations from Negated Statements},
     author = {Blanco, Eduardo and Sarabi, Zahra},
     booktitle = {Proceedings of the 2016 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies},
     month = jun,
     year = {2016},
     address = {San Diego, California},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/N16-1169},
     doi = {10.18653/v1/N16-1169},
     pages = {1431--1441},
     month_numeric = {6}
   }
   

5. Alakananda Vempala and Eduardo Blanco. 23AD. Annotating Temporally-Anchored Spatial Knowledge on Top of OntoNotes Semantic Roles. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), Paris, France. European Language Resources Association (ELRA).

   @inproceedings{VEMPALA16.418,
     author = {Vempala, Alakananda and Blanco, Eduardo},
     title = {Annotating Temporally-Anchored Spatial Knowledge on Top of OntoNotes Semantic Roles},
     booktitle = {Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016)},
     year = {2016},
     month = may,
     date = {23-28},
     location = {Portorož, Slovenia},
     editor = {Chair), Nicoletta Calzolari (Conference and Choukri, Khalid and Declerck, Thierry and Goggi, Sara and Grobelnik, Marko and Maegaard, Bente and Mariani, Joseph and Mazo, Helene and Moreno, Asuncion and Odijk, Jan and Piperidis, Stelios},
     publisher = {European Language Resources Association (ELRA)},
     address = {Paris, France},
     isbn = {978-2-9517408-9-1},
     language = {english},
     url = {https://aclanthology.org/L16-1604/},
     month_numeric = {5}
   }
   

6. Alakananda Vempala and Eduardo Blanco. 2016. Complementing Semantic Roles with Temporally Anchored Spatial Knowledge: Crowdsourced Annotations and Experiments. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, pages 2652–2658, Phoenix, Arizona.

   @inproceedings{DBLP:conf/aaai/VempalaB16,
     author = {Vempala, Alakananda and Blanco, Eduardo},
     title = {Complementing Semantic Roles with Temporally Anchored Spatial Knowledge: Crowdsourced Annotations and Experiments},
     booktitle = {Proceedings of the Thirtieth {AAAI} Conference on Artificial Intelligence},
     pages = {2652--2658},
     address = {Phoenix, Arizona},
     year = {2016},
     url = {http://www.aaai.org/ocs/index.php/AAAI/AAAI16/paper/view/12258}
   }
   

7. H. Andrew Schwartz, Maarten Sap, Margaret L. Kern, Johannes C. Eichstaedt, Adam Kapelner, Megha Agrawal, Eduardo Blanco, Lukasz Dziurzynski, Gregory Park, David Stillwell, Michal Kosinski, Martin E.P. Seligman, and Lyle H. Ungar. 2016. Predicting individual well-being through the language of social media. In Biocomputing 2016: Proceedings of the Pacific Symposium, pages 516–527.

   @inproceedings{schwartz2016predicting,
     year = {2016},
     pages = {516--527},
     booktitle = {Biocomputing 2016: Proceedings of the Pacific Symposium},
     author = {Schwartz, H. Andrew and Sap, Maarten and Kern, Margaret L. and Eichstaedt, Johannes C. and Kapelner, Adam and Agrawal, Megha and Blanco, Eduardo and Dziurzynski, Lukasz and Park, Gregory and Stillwell, David and Kosinski, Michal and Seligman, Martin E.P. and Ungar, Lyle H.},
     title = {Predicting individual well-being through the language of social media},
     url = {https://pubmed.ncbi.nlm.nih.gov/26776214/}
   }
   

2015

1. Eduardo Blanco and Alakananda Vempala. 2015. Inferring Temporally-Anchored Spatial Knowledge from Semantic Roles. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 452–461, Denver, Colorado. Association for Computational Linguistics.

   @inproceedings{blanco-vempala:2015:NAACL-HLT,
     author = {Blanco, Eduardo and Vempala, Alakananda},
     title = {Inferring Temporally-Anchored Spatial Knowledge from Semantic Roles},
     booktitle = {Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
     month = may,
     year = {2015},
     address = {Denver, Colorado},
     publisher = {Association for Computational Linguistics},
     pages = {452--461},
     url = {http://www.aclweb.org/anthology/N15-1048},
     month_numeric = {5}
   }
   

2. Eduardo Blanco and Dan Moldovan. 2015. A Semantic Logic-Based Approach to Determine Textual Similarity. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 23(4):683–693.

   @article{7052346,
     author = {Blanco, Eduardo and Moldovan, Dan},
     journal = {IEEE/ACM Transactions on Audio, Speech, and Language Processing},
     title = {A Semantic Logic-Based Approach to Determine Textual Similarity},
     year = {2015},
     volume = {23},
     number = {4},
     pages = {683-693},
     doi = {10.1109/TASLP.2015.2403613}
   }
   

2014

1. Eduardo Blanco and Dan Moldovan. 2014. Leveraging Verb-Argument Structures to Infer Semantic Relations. In Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics, pages 145–154, Gothenburg, Sweden. Association for Computational Linguistics.

   @inproceedings{blanco-moldovan-2014-leveraging,
     title = {Leveraging Verb-Argument Structures to Infer Semantic Relations},
     author = {Blanco, Eduardo and Moldovan, Dan},
     booktitle = {Proceedings of the 14th Conference of the {E}uropean Chapter of the Association for Computational Linguistics},
     month = apr,
     year = {2014},
     address = {Gothenburg, Sweden},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/E14-1016},
     doi = {10.3115/v1/E14-1016},
     pages = {145--154},
     month_numeric = {4}
   }
   

2. Eduardo Blanco and Dan Moldovan. 2014. Composition of Semantic Relations: Theoretical Framework and Case Study. ACM Transactions on Speech and Language Processing, 10(4). Association for Computing Machinery.

   @article{10.1145/2513146,
     author = {Blanco, Eduardo and Moldovan, Dan},
     title = {Composition of Semantic Relations: Theoretical Framework and Case Study},
     year = {2014},
     issue_date = {December 2013},
     publisher = {Association for Computing Machinery},
     address = {New York, NY, USA},
     volume = {10},
     number = {4},
     issn = {1550-4875},
     url = {https://doi.org/10.1145/2513146},
     doi = {10.1145/2513146},
     abstract = {Extracting semantic relations from text is a preliminary step towards understanding the meaning of text. The more semantic relations are extracted from a sentence, the better the representation of the knowledge encoded into that sentence. This article introduces a framework for the Composition of Semantic Relations (CSR). CSR aims to reveal more text semantics than existing semantic parsers by composing new relations out of previously extracted relations. Semantic relations are defined using vectors of semantic primitives, and an algebra is suggested to manipulate these vectors according to a CSR algorithm. Inference axioms that combine two relations and yield another relation are generated automatically. CSR is a language-agnostic, inventory-independent method to extract semantic relations. The formalism has been applied to a set of 26 well-known relations and results are reported.},
     journal = {ACM Transactions on Speech and Language Processing},
     month = jan,
     articleno = {17},
     numpages = {36},
     keywords = {relation inference, Semantic relations, relation extraction},
     month_numeric = {1}
   }
   

3. Eduardo Blanco and Dan Moldovan. 2014. Retrieving implicit positive meaning from negated statements. Natural Language Engineering, 20(4):501–535. Cambridge University Press.

   @article{blanco_moldovan_2014,
     title = {Retrieving implicit positive meaning from negated statements},
     volume = {20},
     doi = {10.1017/S1351324913000041},
     number = {4},
     journal = {Natural Language Engineering},
     publisher = {Cambridge University Press},
     author = {Blanco, Eduardo and Moldovan, Dan},
     year = {2014},
     pages = {501–535}
   }
   

2013

1. Eduardo Blanco and Dan Moldovan. 2013. A Semantically Enhanced Approach to Determine Textual Similarity. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 1235–1245, Seattle, Washington, USA. Association for Computational Linguistics.

   @inproceedings{blanco-moldovan:2013:EMNLP,
     author = {Blanco, Eduardo and Moldovan, Dan},
     title = {A Semantically Enhanced Approach to Determine Textual Similarity},
     booktitle = {Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing},
     month = oct,
     year = {2013},
     address = {Seattle, Washington, USA},
     publisher = {Association for Computational Linguistics},
     pages = {1235--1245},
     url = {http://www.aclweb.org/anthology/D13-1123},
     month_numeric = {10}
   }
   

2. Hansen Andrew Schwartz, Johannes Eichstaedt, Eduardo Blanco, Lukasz DziurzyÅ„ski, Margaret L. Kern, Stephanie Ramones, Martin Seligman, and Lyle Ungar. 2013. Choosing the Right Words: Characterizing and Reducing Error of the Word Count Approach. In Proceedings of the Second Joint Conference on Lexical and Computational Semantics (*SEM), pages 296–305, Atlanta, Georgia, USA. Association for Computational Linguistics.

   @inproceedings{schwartz-EtAl:2013:*SEM,
     author = {Schwartz, Hansen Andrew and Eichstaedt, Johannes and Blanco, Eduardo and Dziurzyński, Lukasz and Kern, Margaret L. and Ramones, Stephanie and Seligman, Martin and Ungar, Lyle},
     title = {Choosing the Right Words: Characterizing and Reducing Error of the Word Count Approach},
     booktitle = {Proceedings of the Second Joint Conference on Lexical and Computational Semantics {(*SEM)}},
     month = jun,
     year = {2013},
     address = {Atlanta, Georgia, USA},
     publisher = {Association for Computational Linguistics},
     pages = {296--305},
     url = {http://www.aclweb.org/anthology/S13-1042},
     month_numeric = {6}
   }
   

3. Eduardo Blanco and Dan Moldovan. 2013. A Logic Prover Approach to Predicting Textual Similarity. In Proceedings of the 26th Florida Artificial Intelligence Research Society Conference (FLAIRS-26), pages 255–258, St. Pete Beach, Florida.

   @inproceedings{blanco11:logic_sts,
     author = {Blanco, Eduardo and Moldovan, Dan},
     title = {{A Logic Prover Approach to Predicting Textual Similarity}},
     booktitle = {Proceedings of the 26th Florida Artificial Intelligence Research Society Conference (FLAIRS-26)},
     month = may,
     year = {2013},
     pages = {255--258},
     address = {St. Pete Beach, Florida},
     url = {https://www.aaai.org/ocs/index.php/FLAIRS/FLAIRS13/paper/view/5942},
     month_numeric = {5}
   }
   

4. H Andrew Schwartz, Johannes C Eichstaedt, Lukasz Dziurzynski, Margaret L Kern, Eduardo Blanco, Michal Kosinski, David Stillwell, Martin E P Seligman, and Lyle H Ungar. 2013. Toward Personality Insights from Language Exploration in Social Media. In In Proceedings of the AAAI Spring Symposium Series.

   @inproceedings{schwartz2013toward,
     author = {Schwartz, H Andrew and Eichstaedt, Johannes C and Dziurzynski, Lukasz and Kern, Margaret L and Blanco, Eduardo and Kosinski, Michal and Stillwell, David and Seligman, Martin E P and Ungar, Lyle H},
     title = {Toward Personality Insights from Language Exploration in Social Media},
     booktitle = {In Proceedings of the AAAI Spring Symposium Series},
     year = {2013},
     location = {Stanford, CA},
     url = {https://www.aaai.org/ocs/index.php/SSS/SSS13/paper/view/5764}
   }
   

2012

1. Eduardo Blanco Hakki C. Cankaya and Dan Moldovan. 2012. Semantic composition of AT-LOCATION relation with other relations. Natural Language Engineering, 18(3):343–374. Cambridge University Press.

   @article{NLE:8597526,
     author = {Hakki C. Cankaya, Eduardo Blanco and Moldovan, Dan},
     title = {Semantic composition of AT-LOCATION relation with other relations},
     journal = {Natural Language Engineering},
     publisher = {Cambridge University Press},
     volume = {18},
     issue = {3},
     month = jul,
     year = {2012},
     issn = {1469-8110},
     pages = {343--374},
     numpages = {32},
     doi = {10.1017/S1351324911000222},
     url = {http://journals.cambridge.org/article_S1351324911000222},
     month_numeric = {7}
   }
   

2. Eduardo Blanco and Dan Moldovan. 2012. Fine-Grained Focus for Pinpointing Positive Implicit Meaning from Negated Statements. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 456–465, Montréal, Canada. Association for Computational Linguistics.

   @inproceedings{blanco-moldovan:2012:NAACL-HLT,
     author = {Blanco, Eduardo and Moldovan, Dan},
     title = {Fine-Grained Focus for Pinpointing Positive Implicit Meaning from Negated Statements},
     booktitle = {Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
     month = jun,
     year = {2012},
     address = {Montr\'{e}al, Canada},
     publisher = {Association for Computational Linguistics},
     pages = {456--465},
     url = {http://www.aclweb.org/anthology/N12-1050},
     month_numeric = {6}
   }
   

3. Dan Moldovan and Eduardo Blanco. 2012. Polaris: Lymba’s Semantic Parser. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC-2012), pages 66–72, Istanbul, Turkey. European Language Resources Association (ELRA).

   @inproceedings{MOLDOVAN12.176.L12-1040,
     author = {Moldovan, Dan and Blanco, Eduardo},
     url = {http://www.lrec-conf.org/proceedings/lrec2012/pdf/176_Paper.pdf},
     title = {Polaris: Lymba's Semantic Parser},
     booktitle = {Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC-2012)},
     year = {2012},
     month = may,
     address = {Istanbul, Turkey},
     editor = {Calzolari, Nicoletta and Choukri, Khalid and Declerck, Thierry and Do\u{g}an, Mehmet U\u{g}ur and Maegaard, Bente and Mariani, Joseph and Odijk, Jan and Piperidis, Stelios},
     publisher = {European Language Resources Association (ELRA)},
     isbn = {978-2-9517408-7-7},
     language = {English},
     pages = {66--72},
     month_numeric = {5}
   }
   

4. Eduardo Blanco and Dan Moldovan. 2012. Finding Associations between People. In Proceedings of the 25th Florida Artificial Intelligence Research Society Conference (FLAIRS-25), pages 196–201, Marco Isand, FL, USA.

   @inproceedings{blanco11:associations,
     author = {Blanco, Eduardo and Moldovan, Dan},
     title = {{Finding Associations between People}},
     booktitle = {Proceedings of the 25th Florida Artificial Intelligence Research Society Conference (FLAIRS-25)},
     month = may,
     year = {2012},
     pages = {196-201},
     address = {Marco Isand, FL, USA},
     url = {https://www.aaai.org/Library/FLAIRS/flairs12contents.php},
     month_numeric = {5}
   }
   

5. Roser Morante and Eduardo Blanco. 2012. *SEM 2012 Shared Task: Resolving the Scope and Focus of Negation. In *SEM 2012: The First Joint Conference on Lexical and Computational Semantics – Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation (SemEval 2012), pages 265–274, Montréal, Canada. Association for Computational Linguistics.

   @inproceedings{morante-blanco-2012-sem,
     title = {*{SEM} 2012 Shared Task: Resolving the Scope and Focus of Negation},
     author = {Morante, Roser and Blanco, Eduardo},
     booktitle = {*{SEM} 2012: The First Joint Conference on Lexical and Computational Semantics {--} Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation ({S}em{E}val 2012)},
     month = "7-8 " # jun,
     year = {2012},
     address = {Montr{\'e}al, Canada},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/S12-1035},
     pages = {265--274}
   }
   

2011

1. Eduardo Blanco and Dan Moldovan. 2011. Unsupervised Learning of Semantic Relation Composition. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1456–1465, Portland, Oregon, USA. Association for Computational Linguistics.

   @inproceedings{blanco-moldovan-2011-unsupervised,
     title = {Unsupervised Learning of Semantic Relation Composition},
     author = {Blanco, Eduardo and Moldovan, Dan},
     booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
     month = jun,
     year = {2011},
     address = {Portland, Oregon, USA},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/P11-1146},
     pages = {1456--1465},
     month_numeric = {6}
   }
   

2. Eduardo Blanco and Dan Moldovan. 2011. Semantic Representation of Negation Using Focus Detection. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 581–589, Portland, Oregon, USA. Association for Computational Linguistics.

   @inproceedings{blanco-moldovan-2011-semantic,
     title = {Semantic Representation of Negation Using Focus Detection},
     author = {Blanco, Eduardo and Moldovan, Dan},
     booktitle = {Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies},
     month = jun,
     year = {2011},
     address = {Portland, Oregon, USA},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/P11-1059},
     pages = {581--589},
     month_numeric = {6}
   }
   

3. Eduardo Blanco and Dan Moldovan. 2011. Some Issues on Detecting Negation from Text. In Proceedings of the 24th Florida Artificial Intelligence Research Society Conference (FLAIRS-24), pages 228–233, Palm Beach, Florida.

   @inproceedings{blanco11:issues_negation,
     author = {Blanco, Eduardo and Moldovan, Dan},
     title = {{Some Issues on Detecting Negation from Text}},
     booktitle = {Proceedings of the 24th Florida Artificial Intelligence Research Society Conference (FLAIRS-24)},
     month = may,
     year = {2011},
     pages = {228--233},
     address = {Palm Beach, Florida},
     url = {https://aaai.org/ocs/index.php/FLAIRS/FLAIRS11/paper/view/2629},
     month_numeric = {5}
   }
   

4. Eduardo Blanco, Hakki C. Cankaya, and Dan Moldovan. 2011. Commonsense Knowledge Extraction Using Concepts Properties. In Proceedings of the 24th Florida Artificial Intelligence Research Society Conference (FLAIRS-24), pages 222–227, Palm Beach, Florida.

   @inproceedings{blanco11:commonsense_properties,
     author = {Blanco, Eduardo and Cankaya, Hakki C. and Moldovan, Dan},
     title = {{Commonsense Knowledge Extraction Using Concepts Properties}},
     booktitle = {Proceedings of the 24th Florida Artificial Intelligence Research Society Conference (FLAIRS-24)},
     month = may,
     year = {2011},
     pages = {222--227},
     address = {Palm Beach, Florida},
     url = {https://aaai.org/ocs/index.php/FLAIRS/FLAIRS11/paper/view/2642},
     month_numeric = {5}
   }
   

5. Eduardo Blanco and Dan Moldovan. 2011. A Model for Composing Semantic Relations. In Proceedings of the 9th International Conference on Computational Semantics (IWCS 2011), pages 45–54, Oxford, UK.

   @inproceedings{blanco11:model_csr,
     author = {Blanco, Eduardo and Moldovan, Dan},
     title = {{A Model for Composing Semantic Relations}},
     booktitle = {Proceedings of the 9th International Conference on Computational Semantics (IWCS 2011)},
     month = jan,
     year = {2011},
     pages = {45--54},
     address = {Oxford, UK},
     url = {https://aclanthology.org/W11-0106/},
     month_numeric = {1}
   }
   

2010

1. Eduardo Blanco and Dan Moldovan. 2010. Automatic Discovery of Manner Relations and its Applications. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 315–324, Cambridge, MA. Association for Computational Linguistics.

   @inproceedings{blanco-moldovan-2010-automatic,
     title = {Automatic Discovery of Manner Relations and its Applications},
     author = {Blanco, Eduardo and Moldovan, Dan},
     booktitle = {Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing},
     month = oct,
     year = {2010},
     address = {Cambridge, MA},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/D10-1031},
     pages = {315--324},
     month_numeric = {10}
   }
   

2. Eduardo Blanco, Hakki C. Cankaya, and Dan Moldovan. 2010. Composition of Semantic Relations: Model and Applications. In Coling 2010: Posters, pages 72–80, Beijing, China. Coling 2010 Organizing Committee.

   @inproceedings{blanco-cankaya-moldovan:2010:POSTERS,
     author = {Blanco, Eduardo and Cankaya, Hakki C. and Moldovan, Dan},
     title = {Composition of Semantic Relations: Model and Applications},
     booktitle = {Coling 2010: Posters},
     month = aug,
     year = {2010},
     address = {Beijing, China},
     publisher = {Coling 2010 Organizing Committee},
     pages = {72--80},
     url = {http://www.aclweb.org/anthology/C10-2009},
     month_numeric = {8}
   }
   

2008

1. Eduardo Blanco, Nuria Castell, and Dan Moldovan. 2008. Causal Relation Extraction. In Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08), Marrakech, Morocco. European Language Resources Association (ELRA).

   @inproceedings{BLANCO08.87,
     author = {Blanco, Eduardo and Castell, Nuria and Moldovan, Dan},
     title = {Causal Relation Extraction},
     booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
     year = {2008},
     month = may,
     address = {Marrakech, Morocco},
     editor = {},
     publisher = {European Language Resources Association (ELRA)},
     isbn = {2-9517408-4-0},
     language = {english},
     url = {https://aclanthology.org/L08-1175/},
     month_numeric = {5}
   }