Discriminative sentence compression with conditional random fields

Information Processing and Management

Volumn 43, Issue 6, 2007, Pages 1571-1587

  Nomoto, Tadashi ^a  

^a National Institutes for the Humanities   (Japan)

Author keywords

Conditional random fields; Machine learning; Natural language syntax; RSS; Sentence compression; Sequence alignment

Indexed keywords

DATA ACQUISITION; INFORMATION RETRIEVAL; LEARNING SYSTEMS; NONLINEAR SYSTEMS; RSS;

CONDITIONAL RANDOM FIELDS; DATA PAUCITY; SYNTACTIC STRUCTURE;

DATA COMPRESSION;

EID: 34547945995     PISSN: 03064573     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ipm.2007.01.025     Document Type: Article

References (18)

1. Clarke, J., & Lapata, M. (2006). Constraint-based sentence compression: An integer programming approach. In Proceedings of the COLING/ACL (pp. 144-151).
2. Cohen, W. W., Ravikumar, P., & Fienberg, S. E. (2003). A comparison of string distance metrics for name-matching tasks. In Kambhampati, S., Knoblock, C. A. (Eds.), IIWeb (pp. 73-78).
3. Dorr, B., Zajic, D., & Schwartz, R. (2003). Hedge trimmer: a parse-and-trim approach to headline generataion. In Proceedings of the HLT-NAACL text summarization workshop and document understanding conference (DUC03) (pp. 1-8). Edmonton, Canada.
4. Jing, H. (2000). Sentence reduction for automatic text summarization. In Proceedings of the sixth Conference on Applied Natural Language processing (pp. 310-315).
5. Knight K., and Marcu D. Summarization beyond sentence extraction: A probabilistic approach to sentence compression. Artificial Intelligence 139 (2002) 91-107
6. Lafferty, J., MacCallum, A., & Pereira, F. (2001). Conditional random fields: probabilistic models for segmenting and labeling sequence data. In Proceedings of the 18th international conference on machine learning (ICML-2001).
7. Langkilde, I. (2000). Forest-based statistical sentence generation. In Proceedings of the first annual meeting of the North American chapter of the association of computational linguistics. Seattle, WA.
8. McCallum, A., Bellare, K., & Pereira, F. (2005). A conditional random field for discriminatively-trained finite-state string edit distance. In Proceedings of uncertainty in AI (UAI).
9. McDonald, R. (2006). Discriminative sentence compression with soft syntactic evidence. In Proceedings of the 11th conference of EACL (pp. 297-304).
10. Needleman S.B., and Wunsch C.D. A general method applicable to the search for similarities in the amino acid sequences of two proteins. Journal of Molecular Biology 48 (1970) 443-453
11. Nguyen, M. L., Shimazu, A., Horiguchi, S., Ho, B. T., & Hukushi, M. (2004). Probabilistic sentence reduction using support vector machine. In Proceedings of the 20th international conference computational linguistics (COLING-2004) (pp. 743-749).
12. Quattoni, A., Collins, M., & Darrell, T. (2004). Conditional random fields for object recognition. In Proceedings of NIPS 2004.
13. Riezler, S., King, T. H., Crouch, R., & Zaenen, A. (2003). Statistical sentence condensation using ambiguity packing and stochastic disambiguation methods for lexical functional grammar. In Proceedings of HLT-NAACL 2003 (pp.118-125). Edmonton.
14. Sha, F., & Pereira, F. (2003). Shallow parsing with conditional random fields. In Proceedings of the HLT/NAACL-03.
15. Siddharthan, A., Nenkova, A., & McKeown, K. (2004). Syntactic simplification for improving content selection in multi-document summarization. In Proceedings of the 20th International Conference on Computational linguistics (COLING 2004). Geneva, Switzerland.
16. Smith T.F., and Waterman M.S. Identification of common molecular subsequence. Journal of Molecular Biology 147 (1981) 195-197
17. Turner, J., & Charniak, E. (June 2005). Supervised and unsupervised learning for sentence compression. In Proceedings of the 43rd annual meeting of the ACL (pp. 290-297). Ann Arbor.
18. Vandeghinste, V., & Pan, Y. (2004). Sentence compression for automatic subtitling: a hybrid approach. In Proceedings of the ACL workshop on text summarization. Barcelona.