{"type":"ScholarlyArticle","title":"Commonsense Knowledge Enhanced Sentiment Dependency Graph for Sarcasm Detection","authors":[{"name":"Zhe Yu","slug":"zhe-yu","given":"Zhe","family":"Yu","affiliation":"School of New Media and Communication , Tianjin University , Tianjin , China"},{"name":"Di Jin","slug":"di-jin","given":"Di","family":"Jin","affiliation":"School of New Media and Communication , Tianjin University , Tianjin , China; Tianjin Key Laboratory of Cognitive Computing and Application , College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Xiaobao Wang","slug":"xiaobao-wang","given":"Xiaobao","family":"Wang","affiliation":"Tianjin Key Laboratory of Cognitive Computing and Application , College of Intelligence and Computing , Tianjin University , Tianjin , China"},{"name":"Yawen Li","slug":"yawen-li","given":"Yawen","family":"Li","affiliation":"School of Economics and Management , Beijing University of Posts and Telecommunications , Beijing , China"},{"name":"Longbiao Wang","slug":"longbiao-wang","given":"Longbiao","family":"Wang","affiliation":"Tianjin Key Laboratory of Cognitive Computing and Application , College of Intelligence and Computing , Tianjin University , Tianjin , China; Huiyan Technology (Tianjin) Co., Ltd , Tianjin , China"},{"name":"Jianwu Dang","slug":"jianwu-dang","given":"Jianwu","family":"Dang","affiliation":"Peng Cheng Laboratory , Shenzhen , China; Tianjin Key Laboratory of Cognitive Computing and Application , College of Intelligence and Computing , Tianjin University , Tianjin , China"}],"abstract":"Sarcasm is widely utilized on social media platforms such as Twitter and Reddit. Sarcasm detection is required for analyzing people's true feelings since sarcasm is commonly used to portray a reversed emotion opposing the literal meaning. The syntactic structure is the key to make better use of commonsense when detecting sarcasm. However, it is extremely challenging to effectively and explicitly explore the information implied in syntactic structure and commonsense simultaneously. In this paper, we apply the pre-trained COMET model to generate relevant commonsense knowledge, and explore a novel scenario of constructing a commonsense-augmented sentiment graph and a commonsense-replaced dependency graph for each text. Based on this, a Commonsense Sentiment Dependency Graph Convolutional Network (CSDGCN) framework is proposed to explicitly depict the role of external commonsense and inconsistent expressions over the context for sarcasm detection by interactively modeling the sentiment and dependency information. Experimental results on several benchmark datasets reveal that our proposed method beats the state-of-the-art methods in sarcasm detection, and has a stronger interpretability.","doi":"10.24963/ijcai.2023/269","url":"https://ijcai.org/proceedings/2023/269","pdf":"https://ijcai.org/proceedings/2023/0269.pdf","markdown":"https://ijcai.org/proceedings/2023/269.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/269","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":269,"pages":{"first":2423,"last":2431},"keywords":["Machine Learning: ML: Knowledge-aided learning","Natural Language Processing: NLP: Text classification","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Bakshi et al., 2016] Rushlene Kaur Bakshi, Navneet Kaur, Ravneet Kaur, and Gurpreet Kaur. Opinion mining and sentiment analysis. In Proceedings of 3rd international conference on computing for sustainable global develop- ment, pages 452-455, 2016.","title":"Opinion mining and sentiment analysis","authors":"Bakshi","year":"2016","venue":"Proceedings of 3rd international conference on computing for sustainable global development","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bamman and Smith, 2015] David Bamman and Noah Smith. Contextualized sarcasm detection on twitter. In Proceedings of the 29th international Association for the Advance of Artificial Intelligence Conference on Artificial Intelligence, pages 574-577, 2015.","title":"Contextualized sarcasm detection on twitter","authors":"Smith ; ] Bamman, Noah David Bamman, Smith","year":"2015","venue":"Proceedings of the 29th international Association for the Advance of Artificial Intelligence Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bosselut et al., 2019] Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, and Yejin Choi. Comet: Commonsense transformers for au- tomatic knowledge graph construction. arXiv preprint arXiv:1906.05317, 2019.","title":"Comet: Commonsense transformers for automatic knowledge graph construction","authors":"Antoine Bosselut, Hannah Rashkin, Maarten Sap, Chaitanya Malaviya, Asli Celikyilmaz, Yejin Choi","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cambria et al., 2020] Erik Cambria, Yang Li, Frank Z Xing, Soujanya Poria, and Kenneth Kwok. Senticnet 6: Ensem- ble application of symbolic and subsymbolic ai for senti- ment analysis. In Proceedings of the 29th ACM interna- tional conference on information & knowledge manage- ment, pages 105-114, 2020.","title":"Senticnet 6: Ensemble application of symbolic and subsymbolic ai for sentiment analysis","authors":"Erik Cambria, Yang Li, Frank Z Xing, Soujanya Poria, Kenneth Kwok","year":"2020","venue":"Proceedings of the 29th ACM international conference on information & knowledge management","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Chakrabarty et al., 2020] Tuhin Chakrabarty, Deban- jan Ghosh, Smaranda Muresan, and Nanyun Peng. r 3 : Reverse, retrieve, and rank for sarcasm genera- tion with commonsense knowledge. arXiv preprint arXiv:2004.13248, 2020.","title":"r 3 : Reverse, retrieve, and rank for sarcasm generation with commonsense knowledge","authors":"Tuhin Chakrabarty, Debanjan Ghosh, Smaranda Muresan, Nanyun Peng","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Devlin et al., 2018] Jacob Devlin, Ming-Wei Chang, Ken- ton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understand- ing. arXiv preprint arXiv:1810.04805, 2018.","title":"Bert: Pre-training of deep bidirectional transformers for language understanding","authors":"Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ghosh and Veale, 2017] Aniruddha Ghosh and Tony Veale. Magnets for sarcasm: Making sarcasm detection timely, contextual and very personal. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 482-491, 2017.","title":"Magnets for sarcasm: Making sarcasm detection timely, contextual and very personal","authors":"Ghosh, ; ] Aniruddha Veale, Tony Ghosh, Veale","year":"2017","venue":"Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Ghosh et al., 2015] Debanjan Ghosh, Weiwei Guo, and Smaranda Muresan. Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words. In pro- ceedings of the 2015 conference on Empirical Methods in Natural Language Processing, pages 1003-1012, 2015.","title":"Sarcastic or not: Word embeddings to predict the literal or sarcastic meaning of words","authors":"Debanjan Ghosh, Weiwei Guo, Smaranda Muresan","year":"2015","venue":"proceedings of the 2015 conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[González-Ibánez et al., 2011] Roberto González-Ibánez, Smaranda Muresan, and Nina Wacholder. Identifying sarcasm in twitter: a closer look. In Proceedings of the 49th Annual Meeting of the Association for Computa- tional Linguistics: Human Language Technologies, pages 581-586, 2011.","title":"Identifying sarcasm in twitter: a closer look","authors":"Roberto González-Ibánez, Smaranda Muresan, Nina Wacholder","year":"2011","venue":"Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[He et al., 2017] Dongxiao He, Zhiyong Feng, Di Jin, Xi- aobao Wang, and Weixiong Zhang. Joint identification of network communities and semantics via integrative mod- eling of network topologies and node contents. In Pro- ceedings of 31th Association for the Advance of Artificial Intelligence Conference on Artificial Intelligence, 2017.","title":"Joint identification of network communities and semantics via integrative modeling of network topologies and node contents","authors":"Dongxiao He, Zhiyong Feng, Di Jin, Xiaobao Wang, Weixiong Zhang","year":"2017","venue":"Proceedings of 31th Association for the Advance of Artificial Intelligence Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Ilievski et al., 2021a] Filip Ilievski, Alessandro Oltramari, Kaixin Ma, Bin Zhang, Deborah L. McGuinness, and Pe- dro Szekely. Dimensions of commonsense knowledge. Knowledge-Based Systems, 229:107347, 2021.","title":"Dimensions of commonsense knowledge","authors":"Ilievski","year":"2021","venue":"Knowledge-Based Systems","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Ilievski et al., 2021b] Filip Ilievski, Pedro Szekely, and Bin Zhang. Cskg: The commonsense knowledge graph. In Proceeding of 18th European Semantic Web Conference, pages 680-696, 2021.","title":"Cskg: The commonsense knowledge graph","authors":"Ilievski","year":"2021","venue":"Proceeding of 18th European Semantic Web Conference","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Jiang et al., 2020] Nan Jiang, Fang Tian, Jin Li, Xu Yuan, and Jiaqi Zheng. Man: mutual attention neural net- works model for aspect-level sentiment classification in siot. IEEE Internet of Things Journal, 7:2901-2913, 2020.","title":"Man: mutual attention neural networks model for aspect-level sentiment classification in siot","authors":"Nan Jiang, Fang Tian, Jin Li, Xu Yuan, Jiaqi Zheng","year":"2020","venue":"IEEE Internet of Things Journal","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Joshi et al., 2015] Aditya Joshi, Vinita Sharma, and Push- pak Bhattacharyya. Harnessing context incongruity for sarcasm detection. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguis- tics and the 7th International Joint Conference on Natural Language Processing, pages 757-762, 2015.","title":"Harnessing context incongruity for sarcasm detection","authors":"Aditya Joshi, Vinita Sharma, Pushpak Bhattacharyya","year":"2015","venue":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Kim, 2014] Yoon Kim. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Con- ference on Empirical Methods in Natural Language Pro- cessing, pages 1746-1751, 2014.","title":"Convolutional neural networks for sentence classification","authors":"Yoon Kim","year":"2014","venue":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Kipf and Welling, 2016] Thomas N Kipf and Max Welling. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907, 2016.","title":"Semi-supervised classification with graph convolutional networks","authors":"N Thomas, Max Kipf, Welling","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Li et al., 2021] Jiangnan Li, Hongliang Pan, Zheng Lin, Peng Fu, and Weiping Wang. Sarcasm detection with com- monsense knowledge. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 29:3192-3201, 2021.","title":"Sarcasm detection with commonsense knowledge","authors":"Li","year":"2021","venue":"IEEE/ACM Transactions on Audio, Speech, and Language Processing","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liang et al., 2022] Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, and Ruifeng Xu. Multi-modal sarcasm detection via cross-modal graph convolutional network. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, 2022.","title":"Multi-modal sarcasm detection via cross-modal graph convolutional network","authors":"Bin Liang, Chenwei Lou, Xiang Li, Min Yang, Lin Gui, Yulan He, Wenjie Pei, Ruifeng Xu","year":"2022","venue":"Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Liu and others, 2010] Bing Liu et al. Sentiment analysis and subjectivity. Handbook of natural language processing, 2:627-666, 2010.","title":"Sentiment analysis and subjectivity","authors":"Bing Liu, Liu","year":"2010","venue":"Handbook of natural language processing","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Lou et al., 2021] Chenwei Lou, Bin Liang, Lin Gui, Yulan He, Yixue Dang, and Ruifeng Xu. Affective dependency graph for sarcasm detection. In Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pages 1844-1849, 2021.","title":"Affective dependency graph for sarcasm detection","authors":"Lou","year":"2021","venue":"Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Lunando and Purwarianti, 2013] Edwin Lunando and Ayu Purwarianti. Indonesian social media sentiment analysis with sarcasm detection. In Proceedings of the 5th Inter- national Conference on Advanced Computer Science and Information Systems, pages 195-198, 2013.","title":"Edwin Lunando and Ayu Purwarianti. Indonesian social media sentiment analysis with sarcasm detection","authors":"Purwarianti Lunando","year":"2013","venue":"Proceedings of the 5th International Conference on Advanced Computer Science and Information Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Maynard and Greenwood, 2014] Diana G Maynard and Mark A Greenwood. Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analy- sis. In Proceedings of the 9th International Conference on Language Resources and Evaluation, 2014.","title":"Who cares about sarcastic tweets? investigating the impact of sarcasm on sentiment analysis","authors":"G Diana, Mark A Maynard, Greenwood","year":"2014","venue":"Proceedings of the 9th International Conference on Language Resources and Evaluation","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Miao et al., 2020] Zhengjie Miao, Yuliang Li, Xiaolan Wang, and Wang-Chiew Tan. Snippext: Semi-supervised opinion mining with augmented data. In Proceedings of 29th The World Wide Web Conference, pages 617-628, 2020.","title":"Snippext: Semi-supervised opinion mining with augmented data","authors":"Miao","year":"2020","venue":"Proceedings of 29th The World Wide Web Conference","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Pan et al., 2020] Hongliang Pan, Zheng Lin, Peng Fu, and Weiping Wang. Modeling the incongruity between sen- tence snippets for sarcasm detection. In Proceedings of the 24th European Conference on Artificial Intelligence, pages 2132-2139. 2020.","title":"Modeling the incongruity between sentence snippets for sarcasm detection","authors":"Hongliang Pan, Zheng Lin, Peng Fu, Weiping Wang","year":"2020","venue":"Proceedings of the 24th European Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Peters et al., 2019] Matthew E Peters, Mark Neumann, Robert L Logan IV, Roy Schwartz, Vidur Joshi, Sameer Singh, and Noah A Smith. Knowledge en- hanced contextual word representations. arXiv preprint arXiv:1909.04164, 2019.","title":"Knowledge enhanced contextual word representations","authors":"Mark Matthew E Peters, Robert L Neumann, I V Logan, Roy Schwartz, Vidur Joshi, Sameer Singh, Noah A Smith","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Poria et al., 2016] Soujanya Poria, Erik Cambria, Deva- manyu Hazarika, and Prateek Vij. A deeper look into sarcastic tweets using deep convolutional neural networks. arXiv preprint arXiv:1610.08815, 2016.","title":"A deeper look into sarcastic tweets using deep convolutional neural networks","authors":"Poria","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Ptáček et al., 2014] Tomáš Ptáček, Ivan Habernal, and Jun Hong. Sarcasm detection on czech and english twitter. In Proceedings of the 25th International Conference on Com- putational Linguistics, pages 213-223, 2014.","title":"Tomáš Ptáček, Ivan Habernal, and Jun Hong. Sarcasm detection on czech and english twitter","authors":"Ptáček","year":"2014","venue":"Proceedings of the 25th International Conference on Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Riloff et al., 2013] Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, and Ruihong Huang. Sarcasm as contrast between a positive sentiment and negative situation. In Proceedings of the 2013 confer- ence on Empirical Methods in Natural Language Process- ing, pages 704-714, 2013.","title":"Sarcasm as contrast between a positive sentiment and negative situation","authors":"Ellen Riloff, Ashequl Qadir, Prafulla Surve, Lalindra De Silva, Nathan Gilbert, Ruihong Huang","year":"2013","venue":"Proceedings of the 2013 conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":29,"raw":"[Sap et al., 2019] Maarten Sap, Ronan Le Bras, Emily All- away, Chandra Bhagavatula, Nicholas Lourie, Hannah Rashkin, Brendan Roof, Noah A Smith, and Yejin Choi. Atomic: An atlas of machine commonsense for if-then rea- soning. In Proceedings of the AAAI conference on artificial intelligence, volume 33, pages 3027-3035, 2019.","title":"Atomic: An atlas of machine commonsense for if-then reasoning","authors":"Sap","year":"2019","venue":"Proceedings of the AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":30,"raw":"[Speer et al., 2017] Robyn Speer, Joshua Chin, and Cather- ine Havasi. Conceptnet 5.5: An open multilingual graph of general knowledge. In Thirty-first AAAI conference on artificial intelligence, 2017.","title":"Conceptnet 5.5: An open multilingual graph of general knowledge","authors":"Speer","year":"2017","venue":"Thirty-first AAAI conference on artificial intelligence","doi":null,"url":null,"internal_url":null},{"position":31,"raw":"[Storks et al., 2019] Shane Storks, Qiaozi Gao, and Joyce Y Chai. Recent advances in natural language inference: A survey of benchmarks, resources, and approaches. arXiv preprint arXiv:1904.01172, 2019.","title":"Recent advances in natural language inference: A survey of benchmarks, resources, and approaches","authors":"Shane Storks, Qiaozi Gao, Joyce Y Chai","year":"2019","venue":null,"doi":null,"url":null,"internal_url":null},{"position":32,"raw":"[Tang et al., 2020] Hao Tang, Donghong Ji, Chenliang Li, and Qiji Zhou. Dependency graph enhanced dual- transformer structure for aspect-based sentiment classifi- cation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6578- 6588, 2020.","title":"Dependency graph enhanced dualtransformer structure for aspect-based sentiment classification","authors":"Hao Tang, Donghong Ji, Chenliang Li, Qiji Zhou","year":"2020","venue":"Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics","doi":null,"url":null,"internal_url":null},{"position":33,"raw":"[Tay et al., 2018] Yi Tay, Luu Anh Tuan, Siu Cheung Hui, and Jian Su. Reasoning with sarcasm by reading in- between. arXiv preprint arXiv:1805.02856, 2018.","title":"Reasoning with sarcasm by reading inbetween","authors":"Yi Tay, Anh Luu, Siu Cheung Tuan, Jian Hui, Su","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":34,"raw":"[Veale and Hao, 2010] Tony Veale and Yanfen Hao. Detect- ing ironic intent in creative comparisons. In Proceedings of the 14th European Conference on Artificial Intelligence, pages 765-770. 2010.","title":"Detecting ironic intent in creative comparisons","authors":"Hao ; ] Tony Veale, Yanfen Veale, Hao","year":"2010","venue":"Proceedings of the 14th European Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":35,"raw":"[Wang et al., 2020] Xiaobao Wang, Di Jin, Katarzyna Mu- sial, and Jianwu Dang. Topic enhanced sentiment spread- ing model in social networks considering user interest. In Proceedings of the 34th Association for the Advance of Ar- tificial Intelligence Conference on Artificial Intelligence, pages 989-996, 2020.","title":"Topic enhanced sentiment spreading model in social networks considering user interest","authors":"Wang","year":"2020","venue":"Proceedings of the 34th Association for the Advance of Artificial Intelligence Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":36,"raw":"[Wang et al., 2023] Xiaobao Wang, Yiqi Dong, Di Jin, Yawen Li, Longbiao Wang, and Jianwu Dang. Augment- ing affective dependency graph via iterative incongruity graph learning for sarcasm detection. In Proceedings of the 37th Association for the Advance of Artificial Intelli- gence Conference on Artificial Intelligence, 2023.","title":"Augmenting affective dependency graph via iterative incongruity graph learning for sarcasm detection","authors":"Wang","year":"2023","venue":"Proceedings of the 37th Association for the Advance of Artificial Intelligence Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":null},{"position":37,"raw":"[Xiong et al., 2019] Tao Xiong, Peiran Zhang, Hongbo Zhu, and Yihui Yang. Sarcasm detection with self-matching networks and low-rank bilinear pooling. In Proceeding of 28th The World Wide Web Conference, pages 2115-2124, 2019.","title":"Sarcasm detection with self-matching networks and low-rank bilinear pooling","authors":"Tao Xiong, Peiran Zhang, Hongbo Zhu, Yihui Yang","year":"2019","venue":"Proceeding of 28th The World Wide Web Conference","doi":null,"url":null,"internal_url":null},{"position":38,"raw":"[Yang et al., 2016] Zichao Yang, Diyi Yang, Chris Dyer, Xi- aodong He, Alex Smola, and Eduard Hovy. Hierarchical attention networks for document classification. In Pro- ceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies, pages 1480-1489, 2016.","title":"Hierarchical attention networks for document classification","authors":"Yang","year":"2016","venue":"Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}