Structured models for fine-to-coarse sentiment analysis

ACL 2007 - Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics

Volumn , Issue , 2007, Pages 432-439

  McDonald, Ryan ^a   Hannan, Kerry ^a   Neylon, Tyler ^a   Wells, Mike ^a   Reynar, Jeff ^a  

^a GOOGLE INC   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION DECISION; CLASSIFICATION ERRORS; SENTIMENT ANALYSIS; SEQUENCE CLASSIFICATION; STRUCTURED MODEL; VITERBI;

COMPUTATIONAL LINGUISTICS;

TEXT PROCESSING;

EID: 84860520895     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. Y. Choi, C. Cardie, E. Riloff, and S. Patwardhan. 2005. Identifying sources of opinions with conditional random fields and extraction patterns. In Proc. HLT/EMNLP.
2. Y. Choi, E. Breck, and C. Cardie. 2006. Joint extraction of entities and relations for opinion recognition. In Proc. EMNLP.
3. M. Collins. 2002. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proc. EMNLP.
4. K. Crammer and Y. Singer. 2003. Ultraconservative online algorithms for multiclass problems. JMLR.
5. Hal Daumé III, John Langford, and Daniel Marcu. 2006.Search-based structured prediction. In Submission.
6. J. Lafferty, A. McCallum, and F. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In Proc. ICML.
7. P. Liang, A. Bouchard-Cote, D. Klein, and B. Taskar. 2006. An end-to-end discriminative approach to machine translation. In Proc. ACL.
8. Y. Mao and G. Lebanon. 2006. Isotonic conditional random fields and local sentiment flow. In Proc. NIPS.
9. R. McDonald, K. Crammer, and F. Pereira. 2005. Online largemargin training of dependency parsers. In Proc. ACL.
10. R. McDonald. 2006. Discriminative Training and Spanning Tree Algorithms for Dependency Parsing. Ph.D. thesis, University of Pennsylvania.
11. S. Miller, H. Fox, L.A. Ramshaw, and R.M. Weischedel. 2000. A novel use of statistical parsing to extract information from text. In Proc NAACL, pages 226-233.
12. B. Pang and L. Lee. 2004. A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proc. ACL.
13. B. Pang, L. Lee, and S. Vaithyanathan. 2002. Thumbs up? Sentiment classification using machine learning techniques. In EMNLP.
14. A. Popescu and O. Etzioni. 2005. Extracting product features and opinions from reviews. In Proc. HLT/EMNLP.
15. A. Quattoni, M. Collins, and T. Darrell. 2004. Conditional random fields for object recognition. In Proc. NIPS.
16. L. R. Rabiner. 1989. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257-285, February.
17. D. Roth and W. Yih. 2004. A linear programming formulation for global inference in natural language tasks. In Proc. CoNLL.
18. C. Sutton and A. McCallum. 2006. An introduction to conditional random fields for relational learning. In L. Getoor and B. Taskar, editors, Introduction to Statistical Relational Learning. MIT Press.
19. C. Sutton, K. Rohanimanesh, and A. McCallum. 2004. Dynamic conditional random fields: Factorized probabilistic models for labeling and segmenting sequence data. In Proc. ICML.
20. B. Taskar, C. Guestrin, and D. Koller. 2003. Max-margin Markov networks. In Proc. NIPS.
21. B. Taskar, D. Klein, M. Collins, D. Koller, and C. Manning. 2004. Max-margin parsing. In Proc. EMNLP.
22. M. Thomas, B. Pang, and L. Lee. 2006. Get out the vote: Determining support or opposition from congressional floordebate transcripts. In Proc. EMNLP.
23. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. 2004. Support vector learning for interdependent and structured output spaces. In Proc. ICML.
24. P. Turney. 2002. Thumbs up or thumbs down? Sentiment orientation applied to unsupervised classification of reviews. In EMNLP.