DS 2002: Discovery Science pp 302-309 | Cite as
Extraction of Word Senses from Human Factors in Knowledge Discovery
Conference paper
First Online:
Abstract
Flood of information sometimes makes it difficult to extract useful knowledge from databases, libraries and WWW. This paper presents an intelligent method for extraction of word senses from human factors in knowledge discovery, which utilizes the integrated Korean noun and verb networks through the selectional restriction relations in sentences. Integration of Korean Noun Networks into the SENKOV(Semantic Networks for Korean Networks) system will play an important role in both computational linguistic applications and psycholinguistic models of language processing.
Keywords
Machine Translation Semantic Network Logical Constraint Word Sense Noun Class
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Preview
Unable to display preview. Download preview PDF.
References
- 1.Moon, Y.: Design and Implementation of Korean Noun WordNet Based on Semantic Word Concepts. Ph.D. Thesis, Seoul National University, Korea (1996)Google Scholar
- 2.Levin, B.: English Verb Classes and Alterations: A Preliminary Investigation. The MIT Press (1997)Google Scholar
- 3.Roland, D.: Verb Subcategorization Frequency Differences between Business-News and Balanced Corpora: The Role of Verb Sense. Proc. of the Workshop and Comparing Corpora in ACL-2000, Hong Kong (2000)Google Scholar
- 4.Gonzalo, J., Chugur, I., Verdejo, F.: Sense Clusters for Information Retrieval: Evidence from SemCor and the EuroWordNet InterLingual Index. Proc. of SIGLEX Workshop on Word Senses and Multi-linguality in ACL-2000, Hong Kong (2000)Google Scholar
- 5.Resnik, P.: Selection and Information: A Class-Based Approach to Lexical Relationship. Ph.D. Thesis, Univ. of Pennsylvania (1993) 105–114Google Scholar
- 6.Yarowsky, D.: Word-Sense Disambiguation Using Statistical Models of Roget’s Categories Trained on Large Corpora. Proc. Of COLING-92 (1992) 454–460Google Scholar
- 7.Moon Y., Kim, Y.: Concept-Based Verb Translation in the Korean-English Machine Translation System. Journal of Korea Information Science Society, vol. 22, no. 8. Korea (1995) 1166–1173Google Scholar
- 8.Yang, J.: Co-occurrence Similarity of Nouns for Ambiguity Resolution in Analyzing Korean Language. Ph.D. Thesis, Seoul National University (1995)Google Scholar
- 9.Pereira, F., Tishby N., Lee, L.: Distributed Clustering of English Words. Proc. of ACL-93 (1993)Google Scholar
- 10.Miller, G., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: Introduction to WordNet: An On-line Lexical Database. in Five Papers on WordNet, CSL Report. Cognitive Science Laboratory, Princeton University (1993)Google Scholar
- 11.Levin, B., Hovav, M.: Unaccusativity: At the Syntax-Lexical Semantics Interface. The MIT Press (1996)Google Scholar
- 12.Moon, Y.: Design and Implementation of SENKOV System and Its Application to the Selectional Restriction. Proc. of the Workshop MAL in NLPRS (1999) 81–84Google Scholar
- 13.Shin, J., et al.: Verb Classification Utilizing Clustering Techniques. Proc. Of Cognitive Science Society (1999)Google Scholar
Copyright information
© Springer-Verlag Berlin Heidelberg 2002