TY - GEN
T1 - Collective classification of Congressional floor-debate transcripts
AU - Burfoot, Clinton
AU - Bird, Steven
AU - Baldwin, Timothy
PY - 2011/12/1
Y1 - 2011/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84859049367&partnerID=8YFLogxK
M3 - Conference Paper published in Proceedings
AN - SCOPUS:84859049367
SN - 9781932432879
T3 - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies
SP - 1506
EP - 1515
BT - ACL-HLT 2011 - Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics
T2 - 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, ACL-HLT 2011
Y2 - 19 June 2011 through 24 June 2011
ER -