The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Get out the vote: determining support or opposition from congressional floor-debate transcripts
Open accessAbstract
We investigate whether one can determine from the transcripts of U.S. Congressional floor debates whether the speeches represent support of or opposition to proposed legislation. To address this problem, we exploit the fact that these speeches occur as part of a discussion; this allows us to use sources of information regarding relationships between discourse segments, such as whether a given utterance indicates agreement with the opinion expressed by another. We find that the incorporation of such information yields substantial improvements over classifying speeches in isolation.
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Cites
Extracting Policy Positions from Political Texts Using Words as Data
LAVER, M., BENOIT, K., & GARRY, J. (2003) · American Political Science Review · doi:10.1017/s0003055403000698 · Cited by 1,377
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A systematic analysis of performance measures for classification tasks
Sokolova, M., & Lapalme, G. (2009) · Information Processing & Management · doi:10.1016/j.ipm.2009.03.002 · Cited by 7,515
A theory of learning from different domains
Ben-David, S., Blitzer, J., Crammer, K., Kulesza, A., Pereira, F., & Vaughan, J. W. (2010) · Machine Learning · doi:10.1007/s10994-009-5152-4 · Cited by 3,515
(2007) · Cited by 2,018
Advances in natural language processing
Hirschberg, J., & Manning, C. D. (2015) · Science · doi:10.1126/science.aaa8685 · Cited by 1,712
A Method of Automated Nonparametric Content Analysis for Social Science
Hopkins, D. J., & King, G. (2010) · American Journal of Political Science · doi:10.1111/j.1540-5907.2009.00428.x · Cited by 765
Metadata
- Authors
- Thomas, M., Pang, B., & Lee, L.
- Venue
- Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing - EMNLP '06
- Published
- 2006
- Pages
- 327
- OpenAlex
- W1967807490
- Cited by
- 483
- References
- 1
- FWCI
- 11.29
- Type
- conference paper
- Language
- en
- Open access
- bronze
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
