{"type":"ScholarlyArticle","title":"A Deep Modular RNN Approach for Ethos Mining","authors":[{"name":"Rory Duthie","slug":"rory-duthie","given":"Rory","family":"Duthie","affiliation":"Centre for Argument Technology , University of Dundee"},{"name":"Katarzyna Budzynska","slug":"katarzyna-budzynska","given":"Katarzyna","family":"Budzynska","affiliation":"Centre for Argument Technology , Institute of Philosophy and Sociology , Polish Academy of Sciences; Centre for Argument Technology , University of Dundee"}],"abstract":"Automatically recognising and extracting the reasoning expressed in natural language text is extremely\r\ndemanding and only very recently has there been significant headway. While such argument mining focuses on logos (the content of what is said) evidence has demonstrated that using ethos (the character of the speaker) can sometimes be an even more powerful tool of influence. We study the UK parliamentary debates which furnish a rich source of ethos with linguistic material signalling the ethotic relationships between politicians. We then develop a novel deep modular recurrent neural network, DMRNN, approach and employ proven methods from argument mining and sentiment analysis to create an ethos mining pipeline. Annotation of ethotic statements is reliable and its extraction is robust (macro-F1 = 0.83), while annotation of polarity is perfect and its extraction is solid (macro-F1 = 0.84). By exploring correspondences between ethos in political discourse and major events in the political landscape through ethos analytics, we uncover tantalising evidence\r\nthat identifying expressions of positive and negative ethotic sentiment is a powerful instrument for understanding the dynamics of governments.","doi":"10.24963/ijcai.2018/562","url":"https://ijcai.org/proceedings/2018/562","pdf":"https://ijcai.org/proceedings/2018/0562.pdf","markdown":"https://ijcai.org/proceedings/2018/562.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/562","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Natural Language Processing","track":"Main track","number":562,"pages":{"first":4041,"last":4047},"keywords":["Natural Language Processing: Sentiment Analysis and Text Mining","Machine Learning: Deep Learning","Knowledge Representation and Reasoning: Computational Models of Argument","Natural Language Processing: Natural Language Processing"],"videos":[],"references":[{"position":1,"raw":"[Aristotle, 1991] Aristotle. On Rhetoric (G. A. Kennedy, Trans.). New York: Oxford University Press., 1991.","title":"Aristotle. On Rhetoric (G. A. Kennedy, Trans.)","authors":"Aristotle","year":"1991","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Budzynska and Villata, 2016] Katarzyna Budzynska and Serena Villata. Argument mining. In The IEEE Intelligent Informatics Bulletin, volume 17, pages 1-7, 2016.","title":"Argument mining","authors":"Katarzyna Budzynska, Serena Villata","year":"2016","venue":"The IEEE Intelligent Informatics Bulletin","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Duthie et al., 2016] Rory Duthie, Katarzyna Budzynska, and Chris Reed. Mining ethos in political debate. In Proc. of the Sixth Int Conference on Computational Models of Argument (COMMA), pages 299-310. IOS Press, 2016.","title":"Mining ethos in political debate","authors":"Rory Duthie, Katarzyna Budzynska, Chris Reed","year":"2016","venue":"Proc. of the Sixth Int Conference on Computational Models of Argument (COMMA)","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Eger et al., 2017] Steffen Eger, Johannes Daxenberger, and Iryna Gurevych. Neural end-to-end learning for computational argumentation mining. arXiv preprint arXiv:1704.06104, 2017.","title":"Neural end-to-end learning for computational argumentation mining","authors":"Steffen Eger, Johannes Daxenberger, Iryna Gurevych","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Hirst et al., 2014] Graeme Hirst, Yaroslav Riabinin, Jory Graham, Magali Boizot-Roche, and Colin Morris. From text to political positions: Text analysis across disciplines. In Text to ideology or text to party status?, pages 61-79.","title":"From text to political positions: Text analysis across disciplines","authors":"Graeme Hirst, Yaroslav Riabinin, Jory Graham, Magali Boizot-Roche, Colin Morris","year":null,"venue":"Text to ideology or text to party status?","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hochreiter and Schmidhuber, 1997] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural Computation, 9(8):1735-1780, 1997.","title":"Long short-term memory","authors":"Schmidhuber Hochreiter, Sepp Hochreiter, Jürgen Schmidhuber","year":"1997","venue":"Neural Computation","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Ju et al., 2017] Cheng Ju, Aurélien Bibaut, and Mark J van der Laan. The relative performance of ensemble meth- ods with deep convolutional neural networks for image classification. arXiv preprint arXiv:1704.01664, 2017.","title":"The relative performance of ensemble methods with deep convolutional neural networks for image classification","authors":"Cheng Ju, Aurélien Bibaut, Mark J Van Der Laan","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Kingma and Ba, 2014] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014.","title":"Adam: A method for stochastic optimization","authors":"Diederik Kingma, Jimmy Ba","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Lai et al., 2015] Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. Recurrent convolutional neural networks for text classification. In In Proc of the Twenty-Ninth AAAI, vol- ume 333, pages 2267-2273, 2015.","title":"Recurrent convolutional neural networks for text classification","authors":"Siwei Lai, Liheng Xu, Kang Liu, Jun Zhao","year":"2015","venue":"Proc of the Twenty-Ninth AAAI","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Larkey and Croft, 1996] Leah S. Larkey and W. Bruce Croft. Combining classifiers in text categorization. In Proc of the 19th Annual Int Conference on Research and Devel- opment in Information Retrieval, SIGIR, pages 289-297, New York, NY, USA, 1996. ACM.","title":"Combining classifiers in text categorization","authors":"Leah S Larkey, W Bruce Croft","year":"1996","venue":"Proc of the 19th Annual Int Conference on Research and Development in Information Retrieval, SIGIR","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lawrence et al., 2017] John Lawrence, Joonsuk Park, Katarzyna Budzynska, Claire Cardie, Barbara Konat, and Chris Reed. Using argumentative structure to interpret de- bates in online deliberative democracy and erulemaking. ACM Trans. Internet Technol., 17(3):25:1-25:22, 2017.","title":"Using argumentative structure to interpret debates in online deliberative democracy and erulemaking","authors":"John Lawrence, Joonsuk Park, Katarzyna Budzynska, Claire Cardie, Barbara Konat, Chris Reed","year":"2017","venue":"ACM Trans. Internet Technol","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[LeCun et al., 2015] Yann LeCun, Yoshua Bengio, and Ge- offrey Hinton. Deep learning. Nature, 521(7553):436- 444, 2015.","title":"Deep learning","authors":"Yann Lecun, Yoshua Bengio, Geoffrey Hinton","year":"2015","venue":"Nature","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Lee et al., 2017] Jinhyuk Lee, Hyunjae Kim, Miyoung Ko, Donghee Choi, Jaehoon Choi, and Jaewoo Kang. Name nationality classification with recurrent neural networks. In Proc of the Twenty-Sixth Int Joint Conference on Artifi- cial Intelligence, IJCAI-17, pages 2081-2087, 2017.","title":"Name nationality classification with recurrent neural networks","authors":"Lee","year":"2017","venue":"Proc of the Twenty-Sixth Int Joint Conference on Artificial Intelligence, IJCAI-17","doi":null,"url":null,"internal_url":"/proceedings/2017/289"},{"position":14,"raw":"[Liu, 2010] Bing Liu. 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":15,"raw":"[Ma et al., 2016] Lin Ma, Zhengdong Lu, and Hang Li. Learning to answer questions from image using convolu- tional neural network. In Proc of the Thirtieth AAAI, pages 3567-3573. AAAI Press, 2016.","title":"Learning to answer questions from image using convolutional neural network","authors":"Lin Ma, Zhengdong Lu, Hang Li","year":"2016","venue":"Proc of the Thirtieth AAAI","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Naderi and Hirst, 2017] Nona Naderi and Graeme Hirst. Recognizing reputation defence strategies in critical po- litical exchanges. In Proc of the Int Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 527-535, 2017.","title":"Recognizing reputation defence strategies in critical political exchanges","authors":"Hirst Naderi, Nona Naderi, Graeme Hirst","year":"2017","venue":"Proc of the Int Conference Recent Advances in Natural Language Processing","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Pedregosa et al., 2011] F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blon- del, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825-2830, 2011.","title":"Scikit-learn: Machine learning in Python","authors":"F Pedregosa, G Varoquaux, A Gramfort, V Michel, B Thirion, O Grisel, M Blondel, P Prettenhofer, R Weiss, V Dubourg, J Vanderplas, A Passos, D Cournapeau, M Brucher, M Perrot, E Duchesnay","year":"2011","venue":"Journal of Machine Learning Research","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Rheault et al., 2016] Ludovic Rheault, Kaspar Beelen, Christopher Cochrane, and Graeme Hirst. Measuring emo- tion in parliamentary debates with automated textual anal- ysis. PLOS ONE, 11:1-18, 12 2016.","title":"Measuring emotion in parliamentary debates with automated textual analysis","authors":"Ludovic Rheault, Kaspar Beelen, Christopher Cochrane, Graeme Hirst","year":"2016","venue":"PLOS ONE","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Schuster and Manning, 2016] Sebastian Schuster and","title":null,"authors":"Sebastian Schuster","year":null,"venue":null,"doi":null,"url":null,"internal_url":null},{"position":20,"raw":"Christopher D. Manning. Enhanced english universal dependencies: An improved representation for natural language understanding tasks. In LREC 2016, pages 2371-2378, 2016.","title":"Enhanced english universal dependencies: An improved representation for natural language understanding tasks","authors":"D Christopher, Manning","year":"2016","venue":"LREC 2016","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Sutskever et al., 2014] Ilya Sutskever, Oriol Vinyals, and Quoc V Le. Sequence to sequence learning with neural networks. In Advances in Neural Information Processing Systems, pages 3104-3112, 2014.","title":"Quoc V Le. Sequence to sequence learning with neural networks","authors":"Sutskever","year":"2014","venue":"Advances in Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Thomas et al., 2006] Matt Thomas, Bo Pang, and Lillian Lee. Get out the vote: Determining support or opposi- tion from Congressional floor-debate transcripts. In Proc of EMNLP, pages 327-335, 2006.","title":"Get out the vote: Determining support or opposition from Congressional floor-debate transcripts","authors":"Matt Thomas, Bo Pang, Lillian Lee","year":"2006","venue":"Proc of EMNLP","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Toutanova et al., 2003] Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram Singer. Feature-rich part-of-speech tagging with a cyclic dependency network. Proc of HLT-NAACL, pages 252-259, 2003.","title":"Feature-rich part-of-speech tagging with a cyclic dependency network","authors":"Kristina Toutanova, Dan Klein, Christopher Manning, Yoram Singer","year":"2003","venue":"Proc of HLT-NAACL","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2017] Jin Wang, Zhongyuan Wang, Dawei Zhang, and Jun Yan. Combining knowledge with deep convolutional neural networks for short text classification. In Proc of the Twenty-Sixth Int Joint Conference on Artifi- cial Intelligence, IJCAI-17, pages 2915-2921, 2017.","title":"Combining knowledge with deep convolutional neural networks for short text classification","authors":"Jin Wang, Zhongyuan Wang, Dawei Zhang, Jun Yan","year":"2017","venue":"Proc of the Twenty-Sixth Int Joint Conference on Artificial Intelligence, IJCAI-17","doi":null,"url":null,"internal_url":"/proceedings/2017/406"},{"position":25,"raw":"[Zaremba et al., 2014] Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. Recurrent neural network regulariza- tion. arXiv preprint arXiv:1409.2329, 2014.","title":"Recurrent neural network regularization","authors":"Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Zhou et al., 2015] Chunting Zhou, Chonglin Sun, Zhiyuan Liu, and Francis Lau. A C-LSTM neural network for text classification. arXiv preprint arXiv:1511.08630, 2015.","title":"A C-LSTM neural network for text classification","authors":"Zhou","year":"2015","venue":null,"doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Zhu et al., 2015] Xiaodan Zhu, Parinaz Sobhani, and Hongyu Guo. Long short-term memory over recursive structures. In Proc of the 32nd Int Conference on Machine Learning, ICML'15, pages 1604-1612, 2015.","title":"Long short-term memory over recursive structures","authors":"Xiaodan Zhu, Parinaz Sobhani, Hongyu Guo","year":"2015","venue":"Proc of the 32nd Int Conference on Machine Learning, ICML'15","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}