Copy the page URI to the clipboard
Mandya, Angrosh; Siddharthan, Advaith and Wyner, Adam
(2016).
URL: http://www.aclweb.org/anthology/W/W16/W16-2807.pdf
Abstract
This paper describes and evaluates a novel feature set for stance classification of argumentative texts; i.e. deciding whether a post by a user is for or against the issue being debated. We model the debate both as attitude bearing features, including a set of automatically acquired ‘topic terms’ associated with a Distributional Lexical Model (DLM) that captures the writer’s attitude towards the topic term, and as dependency features that represent the points being made in the debate. The stance of the text towards the issue being debated is then learnt in a supervised framework as a function of these features. The main advantage of our feature set is that it is scrutable: The reasons for a classification can be explained to a human user in natural language. We also report that our method outperforms previous approaches to stance classification as well as a range of baselines based on sentiment analysis and topic-sentiment analysis.
Viewing alternatives
Item Actions
Export
About
- Item ORO ID
- 58725
- Item Type
- Conference or Workshop Item
- Extra Information
- Originally presented at ArgMining2016: the Third Workshop on Argument Mining, Berlin, Germany, 7-12 Aug 2016.
- Academic Unit or School
-
Faculty of Science, Technology, Engineering and Mathematics (STEM) > Knowledge Media Institute (KMi)
Faculty of Science, Technology, Engineering and Mathematics (STEM) - Copyright Holders
- © 2016 Association for Computational Linguistics
- Depositing User
- Advaith Siddharthan