A look at parsing and its applications

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2006, Pages 1642-1645

  Lease, Matthew ^a   Charniak, Eugene ^a   Johnson, Mark ^a   McClosky, David ^a  

^a BROWN UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

STATISTICAL PARSING;

ARTIFICIAL INTELLIGENCE; INFORMATION RETRIEVAL;

STATISTICAL METHODS;

EID: 33750735368     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (33)

1. Charniak, E., and Johnson, M. 2001. Edit detection and parsing for transcribed speech. In Proc. NAACL, 118-126.
2. Charniak, E., and Johnson, M. 2005. Coarse-to-fine n-best parsing and MaxEnt discriminative reranking. In Proc. Assoc. for Computational Linguistics (ACL), 173-180.
3. Charniak, E.; Knight, K.; and Yamada, K. 2003. Syntax-based language models for statistical machine translation. In MT Summit IX. Intl. Assoc. for Machine Translation.
4. Charniak, E. 1997. Statistical parsing with a context-free grammar and word statistics. In Proc. AAAI, 598-603.
5. Charniak, E. 2000. A maximum-entropy-inspired parser. In Proc. N. American ACL (NAACL), 132-139.
6. Charniak, E. 2001. Immediate-head parsing for language models. In Proc. Assoc. for Computational Linguistics (ACL), 116-123.
7. Chen, S. F., and Goodman, J. 1996. An empirical study of smoothing techniques for language modeling. In Proc. Assoc. for Computational Linguistics (ACL), 310-318.
8. Collins, M. 1997. Three generative, lexicalised models for statistical parsing. In Proc. Assoc. for Comp. Linguistics, 16-23.
9. Collins, M. 2000. Discriminative reranking for natural language parsing. In Machine Learning: Proc. of the 17th Intl. Conference (ICML 2000), 175-182.
10. Gildea, D. 2001. Corpus variation and parser performance. In Proc. Empirical Methods in NLP (EMNLP), 167-202.
11. Goldwater, S.; Griffiths, T.; and Johnson, M. 2006, Interpolating between types and tokens by estimating power-law generators. In Advances in Neural Information Processing Systems (NIPS) 18
12. Goodman, J. T. 2001. A bit of progress in language modeling extended version. Technical Report 2001-72, Microsoft Research
13. Gregory, M.; Johnson, M.; and Charniak, E. 2004. Sentence-internal prosody does not help parsing the way punctuation does. In Proc. N, American ACL (NAACL), 81-88.
14. Hall, K., and Johnson, M. 2003. Language modelling using efficient best-first bottom-up parsing. In Automatic Speech Recognition and Understanding Workshop, IEEE ASRU 2003.
15. Harper, M., et al. 2005. Johns Hopkins Summer Workshop Final Report on Parsing and Spoken Structural Event Detection. Huang, L., and Chang, D. 2005. Better k-best parsing. Technical Report MS-CIS-05-08, Dept. of Comp. Sci., U. of Pennsylvania.
16. Jimenez, V. M., and Marzal, A. 2000, Computation of the n best parse trees for weighted and stochastic context-free grammars. In Joint Intl. Workshops on Advances in Pattern Recognition (IAPR).
17. Johnson, M., and Charniak, E. 2004. A TAG-based noisy channel model of speech repairs. In Proc. Assoc. for Computational Linguistics, 33-39.
18. Jones, D., et al. 2003. Measuring the readability of automatic speech-to-text transcripts. In Proc. Eurospeech, 1585-1588.
19. Knight, K., and Marcu, D. 2005. Machine translation in the year 2004, In Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP), 965-968.
20. Marcus, M., et al. 1993. Building a large annotated corpus of English: The Penn Treebank. Comp. Linguistics 19(2):313-330.
21. McClosky, D.; Charniak, E.; and Johnson, M. 2006. Effective self-training for parsing. In Proc. N. American ACL (NAACL).
22. Miller, S.; Fox, H.; Ramshaw, L.; and Weischedel, R. 2000. A novel use of statistical parsing to extract information from text. In Proc. N. American ACL (NAACL), 226-233.
23. Park, J. C. 2001. Using combinatory categorical grammar to extract biomedical information. IEEE Intelligent Systems 16(6).
24. Roark, B. 2001. Probabilistic top-down parsing and language modeling. Computational Linguistics 27(2):249-276.
25. Sekine, S. 1997. The domain dependence of parsing. In Proc. Applied Natural Language Processing (ANLP), 96-102.
26. Shieber, S. M., and Tao, X. 2003. Comma restoration using constituency information. In Proc. NAACL, 221-227.
27. Steedman, M., et al. 2003. Bootstrapping statistical parsers from small datasets. In Proc. European ACL (EACL), 331-338.
28. Surdeanu, M.; Harabagiu, S.; Williams, J.; and Aarsçth, P. 2003. Using predicate-argument structures for information extraction. In Proc. Assoc. for Computational Linguistics (ACL), 8-15.
29. Teh, Y. 2006. A bayesian interpretation of interpolated kneserney. Technical report, National University of Singapore.
30. Turner, J., and Charniak, E. 2005. Supervised and unsupervised learning for sentence compression. In Proc. Assoc. for Computational Linguistics (ACL), 290-297.
31. Xu, P., and Jelinek, F. 2004. Random forests in language modeling. In Proc. Empirical Methods in NLP (EMNLP), 325-332.
32. Xu, P.; Chelba, C.; and Jelinek, F. 2002. A study on richer syntactic dependencies for structured language modeling. In Proc. Assoc. for Computational Linguistics (ACL), 191-198.
33. Zettlemoyer, L. S., and Collins, M. 2005. Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Proc. UAI.