A semantics-enhanced language model for unsupervised word sense disambiguation

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4919 LNCS, Issue , 2008, Pages 287-298

  Lin, Shou De ^a   Verspoor, Karin ^b  

^a NATIONAL TAIWAN UNIVERSITY   (Taiwan)

^b LOS ALAMOS NATIONAL LABORATORY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CASE STUDIES; INTELLIGENT TEXT PROCESSING; INTERNATIONAL CONFERENCES; LANGUAGE MODELLING; N-GRAM LANGUAGE MODELS; NLP PROBLEMS; SEMANTIC INFORMATION; SEMANTICALLY ANNOTATED DATA; STANDARD MODEL; WORD-SENSE DISAMBIGUATION;

COMPUTATIONAL LINGUISTICS; INFORMATION THEORY; NATURAL LANGUAGE PROCESSING SYSTEMS; NONLINEAR PROGRAMMING; SEMANTICS; STANDARDS; TEXT PROCESSING; WORD PROCESSING;

LINGUISTICS;

EID: 49949094088     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-78135-6_24     Document Type: Conference Paper

References (18)

1. Banerjee, S., Pedersen, T.: Extended gloss overlaps as a measure of semantic relatedness. In: Proceeding's of the Eighteenth International Joint Conference on Artificial Intelligence, pp. 805-810 (2003)
2. Baum, L.E.: An Inequality and Associated Maximization in Statistical Estimation for Probabilistic Functions of Markov Processes. Inequalities 627(3), 1-8 (1972)
3. Bellegarda, J.: Exploiting latent semantic information in statistical language modeling. Proceedings of IEEE 88(8), 1279-1296 (2000)
4. Brody, S., Navigli, R., Lapata, M.: Ensemble Methods for Unsupervised WSD. In: Proceedings of the ACL/COLING, pp. 97-104 (2006)
5. Brown, P.F., et al.: Class-based n-gram models of natural language. Computational Linguistics 18(4), 467-479 (1992)
6. Chueh, C.H., Wang, H.M., Chien, J.T.: A Maximum Entropy Approach for Semantic Language Modeling. Computational Linguistics and Chinese Language Processing 11(1), 37-56 (2006)
7. Cutting, D., et al.: A practical part-of-speech tagger. In: Proceedings of ANLP1992, Trento, Italy, pp. 133-140 (1992)
8. Dempster, A.D., Laird, N.M., Rubin, D.B.: Maximum likelihood for incomplete data via the EM algorithm. Journal of Royal Statistical Society Series B 39, 1-38 (1977)
9. Galley, M., McKeown, K.: Improving Word Sense Disambiguation in Lexical Chaining. In: Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence, pp. 1486-1488 (2003)
10. Griffiths, T., et al.: Integrating Topics and Syntax. In: Proceedings of the Advances in Neural Information Processing Systems (2004)
11. Hoste, V., et al.: Parameter optimization for machine-learning of word sense disambiguation. Language Engineering 8(4), 311-325 (2002)
12. Knight, K., et al.: Unsupervised Analysis for Decipherment Problems. In: Proceedings of the ACL-COLING (2006)
13. Koehn, P., Knight, K.: Estimating word translation probabilities from unrelated monolingual corpora using the EM algorithm. In: Proceedings of the AAAI, pp. 711-715 (2000)
14. Lin, S.d., Knight, K.: Discovering the linear writing order of a two-dimensional ancient hieroglyphic script. Artificial Intelligence 170(4-5) (2006)
15. McCarthy, D., et al.: Finding predominant word senses in untagged text. In: Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, Barcelona, Spain (2004)
16. Mihalcea, R.: Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling. In: Proceedings of the Joint Conference on Human Language Technology / Empirical Methods in Natural Language Processing (HLT/EMNLP) (2005)
17. Navigli, R., Velardi, P.: Structural Semantic Interconnections: a Knowledge-Based Approach to Word Sense Disambiguation. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 27(7), 1063-1074 (2005)
18. Rapaport, W.J.: Holism, Conceptual-Role Semantics, and Syntactic Semantics. Minds and Machines 12(1), 3-59 (2002)