Paper: Collective Classification of Congressional Floor-Debate Transcripts

ACL ID P11-1151
Title Collective Classification of Congressional Floor-Debate Transcripts
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2011
Authors

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 de- bates. 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 classifica- tion algorithms. Our experimental evaluation shows that the mean-field algorithm obtains the best results for the task, significantly out- performing the benchmark technique.