Back up your Stance: Recognizing Arguments in Online Discussions

In online discussions, users often back up their stance with arguments. Their arguments are often vague, implicit, and poorly worded, yet they provide valuable insights into reasons underpinning users’ opinions. In this paper, we make a first step towards argument-based opinion mining from online discussions and introduce a new task of argument recognition. We match usercreated comments to a set of predefined topic-based arguments, which can be either attacked or supported in the comment. We present a manually-annotated corpus for argument recognition in online discussions. We describe a supervised model based on comment-argument similarity and entailment features. Depending on problem formulation, model performance ranges from 70.5% to 81.8% F1-score, and decreases only marginally when applied to an unseen topic.

[1]  Marie-Francine Moens,et al.  Argumentation mining: the detection, classification and structure of arguments in text , 2009, ICAIL.

[2]  Ido Dagan,et al.  The Third PASCAL Recognizing Textual Entailment Challenge , 2007, ACL-PASCAL@ACL.

[3]  Guido Bugmann,et al.  Training Personal Robots Using Natural Language Instruction , 2001, IEEE Intell. Syst..

[4]  Im Stadtwald,et al.  First-Order Inference and the Interpretation of Questions and Answers , 2000 .

[5]  Marie-Francine Moens,et al.  Automatic detection of arguments in legal texts , 2007, ICAIL.

[6]  Ana Gabriela Maguitman,et al.  An Argument-based Approach to Mining Opinions from Twitter , 2012, AT.

[7]  Lillian Lee,et al.  Opinion Mining and Sentiment Analysis , 2008, Found. Trends Inf. Retr..

[8]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[9]  Marie-Francine Moens,et al.  Language Resources for Studying Argument , 2008, LREC.

[10]  Wei-Hao Lin,et al.  Which Side are You on? Identifying Perspectives at the Document and Sentence Levels , 2006, CoNLL.

[11]  Patrick Pantel,et al.  VerbOcean: Mining the Web for Fine-Grained Semantic Verb Relations , 2004, EMNLP.

[12]  Asher Stern,et al.  Design and realization of a modular architecture for textual entailment , 2013, Natural Language Engineering.

[13]  Raymond H. Putra,et al.  Support or Oppose? Classifying Positions in Online Debates from Reply Activities and Opinion Expressions , 2010, COLING.

[14]  Yuji Matsumoto,et al.  Extracting Aspect-Evaluation and Aspect-Of Relations in Opinion Mining , 2007, EMNLP.

[15]  Jan Snajder,et al.  TakeLab: Systems for Measuring Semantic Text Similarity , 2012, *SEMEVAL.

[16]  S. Erduran,et al.  Argumentation in Science Education: An Overview , 2007 .

[17]  Christiane Fellbaum,et al.  Book Reviews: WordNet: An Electronic Lexical Database , 1999, CL.

[18]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[19]  Uzay Kaymak,et al.  Mining Economic Sentiment Using Argumentation Structures , 2010, ER Workshops.

[20]  Marilyn A. Walker,et al.  Cats Rule and Dogs Drool!: Classifying Stance in Online Debate , 2011, WASSA@ACL.

[21]  Rob Malouf,et al.  Taking sides: user classification for informal online political discourse , 2008, Internet Res..

[22]  Phan Minh Dung,et al.  On the Acceptability of Arguments and its Fundamental Role in Nonmonotonic Reasoning, Logic Programming and n-Person Games , 1995, Artif. Intell..

[23]  Serena Villata,et al.  Combining Textual Entailment and Argumentation Theory for Supporting Online Debates Interactions , 2012, ACL.

[24]  Ana Gabriela Maguitman,et al.  A First Approach to Mining Opinions as Multisets through Argumentation , 2013, AT.

[25]  J Quinonero Candela,et al.  Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment , 2006, Lecture Notes in Computer Science.