Collective classification of Congressional floor-debate transcripts

Clinton Burfoot, Steven Bird, Timothy Baldwin

Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedings

Abstract

This paper explores approaches to sentiment classification of U.S. Congressional floor-debate transcripts. Collective classification techniques are used to take advantage of the informal citation structure present in the debates. We use a range of methods based on local and global formulations and introduce novel approaches for incorporating the outputs of machine learners into collective classification algorithms. Our experimental evaluation shows that the mean-field algorithm obtains the best results for the task, significantly outperforming the benchmark technique.

Original languageEnglish
Title of host publicationACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies
Pages1506-1515
Number of pages10
Publication statusPublished - 1 Dec 2011
Externally publishedYes
Event49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011 - Portland, OR, United States
Duration: 19 Jun 201124 Jun 2011

Publication series

NameACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
Volume1

Conference

Conference49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
CountryUnited States
CityPortland, OR
Period19/06/1124/06/11

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Burfoot, C., Bird, S., & Baldwin, T. (2011). Collective classification of Congressional floor-debate transcripts. In ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies (pp. 1506-1515). (ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies; Vol. 1).