Collective classification of congressional floor-debate transcripts

Authors:
Clinton Burfoot;Steven Bird;Timothy Baldwin
Affiliations:
University of Melbourne, Australia;University of Melbourne, Australia;University of Melbourne, Australia
Venue:
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Year:
2011

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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.