A Model for Word and Context Identification and Disambiguation
Abstract
This paper focus on the interpretation of polisemic words and contexts. It suggests that this interpretation is context-sensitive, regardless of the inner representation of the words themselves. Context is viewed as a cluster formed by a target-word and the words co-ocurring with it. This cluster is achieved by means of statistical treatment of texts as well as graphs representing data obtained in the processing. Preliminary results show that this kind of approach is promising in terms of polisemic words disambiguation.References
Cruse, D. (1986) Lexical Semantics. Cambridge: CUP.
Farkas, I.& Li, P. (2002) “Modeling the development of lexicon with a growing self-organizing map”, Proceedings of the 6th Joint Conference on Information Sciences, Research Triangle Park, NC, p. 553-556.
Fillmore, C. & Atkins, B. (2000) “Describing polisemy: the case of ‘Crawl’ ”, In: Ravin & Leacock (eds.). Polysemy. Theoretical and computational approaches. Oxford: Oxford University Press.
Firth, J. R. (1957). Papers in Linguistics – 1934-1951. Oxford: Oxford University Press.
Manning, C & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, Massachusetts: The MIT Press.
Schütze, H. (1998). “Automatic Word Sense Discrimination”, Computational Linguistics, 24(1), 97-123.
Schütze, H & J. Pedersen.(1995). “Information retrieval based on word senses”, Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, EUA, p. 161-175.
Taylor, J. (2003). “Polisemy’s paradoxes”, Language Sciences (25) 637-655.
Widdows, D. (2003). “A Mathematical Model for Context and Word-Meaning”, Fourth International and Interdisciplinary Conference on Modeling and Using Context, Stanford, California, June 2003, pp. 369-382.
Widdows, D & Dorow, B. (2002). “A Graph Model for Unsupervised Lexical Acquisition”, Proceedings of the 19th International Conference on Computational Linguistics (COLING 2002), Taipei, Taiwan, p.1093-1099.
Farkas, I.& Li, P. (2002) “Modeling the development of lexicon with a growing self-organizing map”, Proceedings of the 6th Joint Conference on Information Sciences, Research Triangle Park, NC, p. 553-556.
Fillmore, C. & Atkins, B. (2000) “Describing polisemy: the case of ‘Crawl’ ”, In: Ravin & Leacock (eds.). Polysemy. Theoretical and computational approaches. Oxford: Oxford University Press.
Firth, J. R. (1957). Papers in Linguistics – 1934-1951. Oxford: Oxford University Press.
Manning, C & Schütze, H. (1999). Foundations of Statistical Natural Language Processing. Cambridge, Massachusetts: The MIT Press.
Schütze, H. (1998). “Automatic Word Sense Discrimination”, Computational Linguistics, 24(1), 97-123.
Schütze, H & J. Pedersen.(1995). “Information retrieval based on word senses”, Proceedings of the 4th Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, EUA, p. 161-175.
Taylor, J. (2003). “Polisemy’s paradoxes”, Language Sciences (25) 637-655.
Widdows, D. (2003). “A Mathematical Model for Context and Word-Meaning”, Fourth International and Interdisciplinary Conference on Modeling and Using Context, Stanford, California, June 2003, pp. 369-382.
Widdows, D & Dorow, B. (2002). “A Graph Model for Unsupervised Lexical Acquisition”, Proceedings of the 19th International Conference on Computational Linguistics (COLING 2002), Taipei, Taiwan, p.1093-1099.
Published
2004-07-31
How to Cite
ARANHA, Christian Nunes; FREITAS, Maria Cláudia de; DIAS, Maria Carmelita Pádua; PASSOS, Emmanuel Lopes.
A Model for Word and Context Identification and Disambiguation. In: BRAZILIAN SYMPOSIUM IN INFORMATION AND HUMAN LANGUAGE TECHNOLOGY (STIL), 2. , 2004, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2004
.
p. 94-103.
