Differentiating homonymy and polysemy in information retrieval

HLT/EMNLP 2005 - Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Volumn , Issue , 2005, Pages 403-410

  Stokoe, Christopher ^a  

^a UNIVERSITY OF SUNDERLAND   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

INFORMATION RETRIEVAL;

RETRIEVAL EFFECTIVENESS; WEB RETRIEVAL; WORD SENSE; WORD SENSE DISAMBIGUATION;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 80053251268     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3115/1220575.1220626     Document Type: Conference Paper

References (21)

1. Agirre E. and Martinez D. 2004. Unsupervised WSD based on automatically retrieved examples: the importance of bias, in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP). Barcelona, Spain.
2. Gale, W; Church, K. W; Yarowsky, D. 1992 Work on Statistical Methods for Word Sense Disambiguation, in Intelligent Probabilistic Approaches to Natural Language Papers, AAAI Press, Pp. 54 - 60.
3. Gaustad, T. 2001. Statistical Corpus-based Word Sense Disambiguation: Pseudowords vs. Real Ambiguous Words, in Companion Volume to the Proceedings of the 36th annual meeting of the ACL, Toulouse, France. Proceedings of the Student Research Workshop, ACL Press, Pp. 61 - 66.
4. Gonzalo, J; Verdejo, F; Chugur, I; Cigarran, J. 1998. Indexing with WordNet Synsets Can Improve Text Retrieval, in Proceedings of COLING/ACL '98 Workshop on the Usage of WordNet for NLP, Montreal, Canada, ACL Press, Pp. 38 - 44.
5. th Text REtrieval Conference, Gaithersburg, MD. NIST Special Publication 500-250, Pp. 61 - 67.
6. Kilgarriff, A. 1992. Polysemy, Ph.D. Thesis, School of Cognitive and Computing Sciences, University of Sussex, Report CSRP 261.
7. th ACM SIGIR, Sheffield, UK. Pp. 258 - 265.
8. Krovetz, R and Croft, W. B. 1992. Lexical Ambiguity and Information Retrieval, ACM Transactions on Information Systems, 10(2), Pp. 115 - 141. (Pubitemid 23599678)
9. Krovetz, R 1997. Homonymy and Polysemy in Information Retrieval, NEC Institute, Princeton.
10. Leacock, C; Chodorow, M; Miller, G. A. 1998 Using Corpus Statistics and WordNet Relations for Sense Identification. Computational Linguistics, 24(1), Pp. 147 - 165.
11. rd International Workshop on the Evaluation of Systems for the Semantic Analysis of Text, Barcelona, Spain, Pp. 25 - 28.
12. Miller, G; Beckwith, R; Fellbaum, C; Gross, D; Miller, K. 1990. WordNet: An On-line Lexical Database, International journal of lexicography, 3(4), Oxford University Press, Pp 235 - 244.
13. Nakov, P. and Hearst, M. 2003. Category-based Pseudowords, in Proceedings of the Human Language Technology Conference (HLT-NAACL 2003), Edmonton. Pp. 67-69.
14. Salton, G and McGill, M. J. 1983. Introduction to Modern Information Retrieval, McGraw-Hill.
15. Sanderson, M. 1994. Word Sense Disambiguation and Information Retrieval, in Proceedings of the 17th ACM SIGIR, Dublin, Ireland, ACM Press, Pp. 142 - 151.
16. Sanderson, M and Van Rijsbergen, C. J. 1999. The Impact on Retrieval Effectiveness of Skewed Frequency Distributions, ACM Transactions in Information Systems 17(4), ACM Press, Pp. 440 - 465. (Pubitemid 129541516)
17. Sanderson, M. 2000. Retrieving with Good Sense in Information Retrieval 2(1), Kluwer Academic Publishing, Pp. 49 - 69.
18. Schütze, H. 1998. Automatic Word Sense Disambiguation, Computational Linguistics 24(1), Pp. 113-120.
19. th Symposium on Document Analysis and Information Retrieval, Pp. 161-175.
20. th ACM SIGIR, Toronto, Canada, Pp. 159 - 166.
21. Zipf, G. K. 1949. Human Behavior and the Principle of Least-Effort, Cambridge MA, U.S.A., Addison-Wesley.