Combining Argument Mining Techniques

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, we look at three different methods of extracting the argumentative structure from a piece of natural language text. These methods cover linguistic features, changes in the topic being discussed and a supervised machine learning approach to identify the components of argumentation schemes, patterns of human seasoning which have been detailed extensively in philosophy and psychology. For each of these approaches we achieve results comparable to those previously reported, whilst at the same time achieving a more detailed argument structure. Finally, we use the results from these individual techniques to apply them in combination, further improving the argument structure identification.
Original languageEnglish
Title of host publication2nd Workshop on Argumentation Mining
Subtitle of host publication Proceedings of the Workshop
Place of PublicationDenver
PublisherAssociation for Computational Linguistics
Pages127-136
Number of pages10
Volume1
ISBN (Print)978-1-941643-34-1
Publication statusPublished - Jun 2015
Event2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015): 2nd Workshop on Argumentation Mining 2015 - Sheraton Denver Downtown Hotel, Denver, United States
Duration: 31 May 20155 Jun 2015
http://naacl.org/naacl-hlt-2015/

Conference

Conference2015 Conference of the North American Chapter of the Association for Computational Linguistics – Human Language Technologies (NAACL HLT 2015)
CountryUnited States
CityDenver
Period31/05/155/06/15
Internet address

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  • Cite this

    Lawrence, J., & Reed, C. (2015). Combining Argument Mining Techniques. In 2nd Workshop on Argumentation Mining: Proceedings of the Workshop (Vol. 1, pp. 127-136). Association for Computational Linguistics. http://aclweb.org/anthology/W/W15/