{"type":"ScholarlyArticle","title":"Stance Classification with Target-specific Neural Attention","authors":[{"name":"Jiachen Du","slug":"jiachen-du","given":"Jiachen","family":"Du","affiliation":"Laboratory of Network Oriented Intelligent Computation , Shenzhen Graduate School , Harbin Institute of Technology , Shenzhen , China; Department of Computing , the Hong Kong Polytechnic University , Hong Kong"},{"name":"Ruifeng Xu","slug":"ruifeng-xu","given":"Ruifeng","family":"Xu","affiliation":"Laboratory of Network Oriented Intelligent Computation , Shenzhen Graduate School , Harbin Institute of Technology , Shenzhen , China; Guangdong Provincial Engineering Technology Research Center for Data Science , Guangzhou , China"},{"name":"Yulan He","slug":"yulan-he","given":"Yulan","family":"He","affiliation":"School of Engineering and Applied Science , Aston University , United Kingdom"},{"name":"Lin Gui","slug":"lin-gui","given":"Lin","family":"Gui","affiliation":"Laboratory of Network Oriented Intelligent Computation , Shenzhen Graduate School , Harbin Institute of Technology , Shenzhen , China"}],"abstract":"Stance classification, which aims at detecting the stance expressed in text towards a specific target, is an emerging problem in sentiment analysis. A major difference between stance classification and traditional aspect-level sentiment classification is that the identification of stance is dependent on target which might not be explicitly mentioned in text. This indicates that apart from text content, the target information is important to stance detection. To this end, we propose a neural network-based model, which incorporates target-specific information into stance classification by following a novel attention mechanism. In specific, the attention mechanism is expected to locate the critical parts of text which are related to target. Our evaluations on both the English and Chinese Stance Detection datasets show that the proposed model achieves the state-of-the-art performance.","doi":"10.24963/ijcai.2017/557","url":"https://ijcai.org/proceedings/2017/557","pdf":"https://ijcai.org/proceedings/2017/0557.pdf","markdown":"https://ijcai.org/proceedings/2017/557.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/557","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Natural Language Processing","track":"Main track","number":557,"pages":{"first":3988,"last":3994},"keywords":["Machine Learning: Deep Learning","Natural Language Processing: Sentiment Analysis and Text Mining","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Augenstein et al., 2016] Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, and Kalina Bontcheva. Stance detection with bidirectional conditional encod- ing. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pages 876-885, 2016.","title":"Stance detection with bidirectional conditional encoding","authors":"Isabelle Augenstein, Tim Rocktäschel, Andreas Vlachos, Kalina Bontcheva","year":"2016","venue":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bahdanau et al., 2014] Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.","title":"Neural machine translation by jointly learning to align and translate","authors":"Dzmitry Bahdanau, Kyunghyun Cho, Yoshua Bengio","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Elman, 1990] Jeffrey L Elman. Finding structure in time. Cognitive science, 14(2):179-211, 1990.","title":"Finding structure in time","authors":"Jeffrey L Elman","year":"1990","venue":"Cognitive science","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Faulkner, 2014] Adam Faulkner. Automated classification of stance in student essays: An approach using stance target information and the wikipedia link-based measure. Science, 376(12):86, 2014.","title":"Automated classification of stance in student essays: An approach using stance target information and the wikipedia link-based measure","authors":"Adam Faulkner, Faulkner","year":"2014","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Ferreira and Vlachos, 2016] William Ferreira and Andreas Vlachos. Emergent: a novel data-set for stance classifica- tion. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1163- 1168, 2016.","title":"Emergent: a novel data-set for stance classification","authors":"Vlachos Ferreira, William Ferreira, Andreas Vlachos","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},{"position":6,"raw":"[Gui et al., 2016] Lin Gui, Ruifeng Xu, Yulan He, Qin Lu, and Zhongyu Wei. Intersubjectivity and sentiment: from language to knowledge. In Proceedings of International Joint Conference on Artificial Intelligence, pages 2789- 2795, 2016.","title":"Intersubjectivity and sentiment: from language to knowledge","authors":"Lin Gui, Ruifeng Xu, Yulan He, Qin Lu, Zhongyu Wei","year":"2016","venue":"Proceedings of International Joint Conference on Artificial Intelligence","doi":null,"url":null,"internal_url":"/proceedings/2016/396"},{"position":7,"raw":"[Hasan and Ng, 2013] Kazi Saidul Hasan and Vincent Ng. Stance classification of ideological debates: Data, mod- els, features, and constraints. In The 4th International Joint Conference on Natural Language Processing, pages 1348-1356, 2013.","title":"Stance classification of ideological debates: Data, models, features, and constraints","authors":"Ng ; ] Hasan, Saidul Kazi, Vincent Hasan, Ng","year":"2013","venue":"The 4th International Joint Conference on Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jrgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jrgen Schmidhuber","year":"1997","venue":"Neural computation","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liu, 2012] Bing Liu. Sentiment analysis and opinion min- ing. Synthesis lectures on human language technologies, 5(1):1-167, 2012.","title":"Sentiment analysis and opinion mining","authors":"Bing Liu, Liu","year":"2012","venue":"Synthesis lectures on human language technologies","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781, 2013.","title":"Efficient estimation of word representations in vector space","authors":"Tomas Mikolov, Kai Chen, Greg Corrado, Jeffrey Dean","year":"2013","venue":null,"doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Mohammad et al., 2016] Saif M Mohammad, Svetlana Kir- itchenko, Parinaz Sobhani, Xiaodan Zhu, and Colin Cherry. Semeval-2016 task 6: Detecting stance in tweets. Proceedings of the 9th International Workshop on Seman- tic Evaluation, pages 31-41, 2016.","title":"Semeval-2016 task 6: Detecting stance in tweets","authors":"M Saif, Svetlana Mohammad, Parinaz Kiritchenko, Xiaodan Sobhani, Colin Zhu, Cherry","year":"2016","venue":"Proceedings of the 9th International Workshop on Semantic Evaluation","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Rush et al., 2015] Alexander M Rush, Sumit Chopra, and Jason Weston. A neural attention model for abstractive sentence summarization. In Proceedings of the 2015 Con- ference on Empirical Methods in Natural Language Pro- cessing, pages 379-389, 2015.","title":"A neural attention model for abstractive sentence summarization","authors":"Sumit Alexander M Rush, Jason Chopra, Weston","year":"2015","venue":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Tai et al., 2015] Kai Sheng Tai, Richard Socher, and Christopher D Manning. Improved semantic representa- tions from tree-structured long short-term memory net- works. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th In- ternational Joint Conference on Natural Language Pro- cessing, pages 1556-1566, 2015.","title":"Improved semantic representations from tree-structured long short-term memory networks","authors":"Kai Sheng, Tai, Richard Socher, Christopher D Manning","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":14,"raw":"[Tang et al., 2015] Duyu Tang, Bing Qin, and Ting Liu. Doc- ument modeling with gated recurrent neural network for sentiment classification. In Proceedings of the 2015 Con- ference on Empirical Methods in Natural Language Pro- cessing, pages 1422-1432, 2015.","title":"Document modeling with gated recurrent neural network for sentiment classification","authors":"Tang","year":"2015","venue":"Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Vijayaraghavan et al., 2016] Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, and Deb Roy. Deep- stance at semeval-2016 task 6: Detecting stance in tweets using character and word-level cnns. arXiv preprint arXiv:1606.05694, 2016.","title":"Deepstance at semeval-2016 task 6: Detecting stance in tweets using character and word-level cnns","authors":"Prashanth Vijayaraghavan, Ivan Sysoev, Soroush Vosoughi, Deb Roy","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Walker et al., 2012] Marilyn A Walker, Pranav Anand, Robert Abbott, and Ricky Grant. Stance classification us- ing dialogic properties of persuasion. In Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Lan- guage Technologies, pages 592-596, 2012.","title":"Stance classification using dialogic properties of persuasion","authors":"Marilyn A Walker, Pranav Anand, Robert Abbott, Ricky Grant","year":"2012","venue":"Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Wang et al., 2016] Yequan Wang, Minlie Huang, Li Zhao, and Xiaoyan Zhu. Attention-based lstm for aspect-level sentiment classification. In Proceedings of the 2016 Con- ference on Empirical Methods in Natural Language Pro- cessing, pages 606-615, 2016.","title":"Attention-based lstm for aspect-level sentiment classification","authors":"Wang","year":"2016","venue":"Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Xu et al., 2015] Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C Courville, Ruslan Salakhutdi- nov, Richard S Zemel, and Yoshua Bengio. Show, attend and tell: Neural image caption generation with visual at- tention. In Proceedings of the 32nd International Confer- ence on Machine, pages 77-81, 2015.","title":"Show, attend and tell: Neural image caption generation with visual attention","authors":"Kelvin Xu, Jimmy Ba, Ryan Kiros, Kyunghyun Cho, Aaron C Courville, Ruslan Salakhutdinov, Richard S Zemel, Yoshua Bengio","year":"2015","venue":"Proceedings of the 32nd International Conference on Machine","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Xu et al., 2016] Ruifeng Xu, Yu Zhou, Dongyin Wu, Lin Gui, Jiachen Du, and Yun Xue. Overview of nlpcc shared task 4: Stance detection in chinese microblogs. In Pro- ceedings of Internatial Conference on Computer Process- ing of Oriental Lanugaues and National CCF Confernece on Natural Language Processing and Chinese Computing, pages 907-916, 2016.","title":"Overview of nlpcc shared task 4: Stance detection in chinese microblogs","authors":"Ruifeng Xu, Yu Zhou, Dongyin Wu, Lin Gui, Jiachen Du, Yun Xue","year":"2016","venue":"Proceedings of Internatial Conference on Computer Processing of Oriental Lanugaues and National CCF Confernece on Natural Language Processing and Chinese Computing","doi":null,"url":null,"internal_url":null},{"position":20,"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."}