A hybrid Web-based measure for computing semantic relatedness between words

Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI

Volumn , Issue , 2009, Pages 441-448

  Spanakis, Gerasimos ^a   Siolas, Georgios ^a   Stafylopatis, Andreas ^a  

^a NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

BENCHMARK DATASETS; HYPERNYMS; REGRESSION PROBLEM; SEMANTIC RELATEDNESS; SEMANTICALLY-RELATED WORDS; SIMILARITY SCORES; WEB SEARCH ENGINES; WORDNET;

ARTIFICIAL INTELLIGENCE; INFORMATION RETRIEVAL; INFORMATION SERVICES; NATURAL LANGUAGE PROCESSING SYSTEMS; SEARCH ENGINES; SEMANTIC WEB; SEMANTICS;

WORLD WIDE WEB;

EID: 77949505983     PISSN: 10823409     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICTAI.2009.64     Document Type: Conference Paper

References (25)

1. D. Bollegala, Y. Matsuo, and M. Ishizuka. Measuring semantic similarity between words using web search engines. In WWW '07: Proceedings of the 16th international conference on World Wide Web, pages 757-766, New York, NY, USA, 2007. ACM.
2. A. Budanitsky and G. Hirst. Evaluating wordnet-based measures of lexical semantic relatedness. Comput. Linguist., 32(1):13-47, 2006.
3. H.-H. Chen, M.-S. Lin, and Y.-C. Wei. Novel association measures using web search with double checking. In ACL- 44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics, pages 1009-1016, Morristown, NJ, USA, 2006. Association for Computational Linguistics.
4. P. Cimiano, S. Handschuh, and S. Staab. Towards the self-annotating web. In WWW '04: Proceedings of the 13th international conference on World Wide Web, pages 462-471, New York, NY, USA, 2004. ACM.
5. R. Collobert, S. Bengio, and C.Williamson. Svmtorch: Support vector machines for large-scale regression problems. Journal of Machine Learning Research, 1:143-160, 2001.
6. C. Fellbaum, editor. WordNet: An Electronic Lexical Database (Language, Speech, and Communication). The MIT Press, May 1998.
7. L. Finkelstein, E. Gabrilovich, Y. Matias, E. Rivlin, Z. Solan, G.Wolfman, and E. Ruppin. Placing search in context: the concept revisited. ACM Trans. Inf. Syst., 20(1):116-131, 2002.
8. G. Forman. A pitfall and solution in multi-class feature selection for text classification. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, page 38, New York, NY, USA, 2004. ACM.
9. G. Grefenstette. Sextant: Exploring unexplored contexts for semantic extraction from syntactic analysis. In Proceedings of the 30st annual meeting of the Association for Computational Linguistics, ACL, pages 324-329, 1992.
10. E. Iosif and A. Potamianos. Unsupervised semantic similarity computation usingweb search engines. In WI '07: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence, pages 381-387, Washington, DC, USA, 2007. IEEE Computer Society.
11. M. Jarmasz. Roget"s thesaurus as a lexical resource for natural language processing. Technical report, University of Ottawa, 2003.
12. J. J. Jiang and D. W. Conrath. Semantic similarity based on corpus statistics and lexical taxonomy. In International Conference Research on Computational Linguistics (ROCLING X), pages 9008+, September 1997.
13. T. Joachims. Making large-scale support vector machine learning practical. Advances in kernel methods: support vector learning, pages 169-184, 1999.
14. D. Lin. Automatic retrieval and clustering of similar words. In Proceedings of the 17th international conference on Computational linguistics, pages 768-774, Morristown, NJ, USA, 1998. Association for Computational Linguistics.
15. C. D. Manning and H. Sch́utze. Foundations of Statistical Natural Language Processing. The MIT Press, June 1999.
16. G. A. Miller and W. G. Charles. Contextual correlates of semantic similarity. Language and Cognitive Processes, 6(1):1-28, 1991.
17. P. Resnik. Using information content to evaluate semantic similarity in a taxonomy. In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 448-45, 1995.
18. P. Resnik. Semantic similarity in a taxonomy: An information-based measure and its application to problems of ambiguity in natural language. Journal of Artificial Intelligence Research, 11:95-130, 1999.
19. M. Sahami and T. D. Heilman. A web-based kernel function for measuring the similarity of short text snippets. In WWW '06: Proceedings of the 15th international conference on World Wide Web, pages 377-386, New York, NY, USA, 2006. ACM.
20. G. Salton and M. J. Mcgill. Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York, NY, USA, 1986.
21. G. Salton, A. Wong, and C. S. Yang. A vector space model for automatic indexing. Commun. ACM, 18(11):613-620, 1975.
22. F. Sebastiani and C. N. D. Ricerche. Machine learning in automated text categorization. ACM Computing Surveys, 34:1-47, 2002.
23. I. Tsochantaridis, T. Hofmann, T. Joachims, and Y. Altun. Support vector machine learning for interdependent and structured output spaces. In ICML '04: Proceedings of the twenty-first international conference on Machine learning, page 104, New York, NY, USA, 2004. ACM.
24. P. D. Turney. Mining the web for synonyms: PMI-IR versus LSA on TOEFL. Lecture Notes in Computer Science, 2167:491-502, 2001.
25. B. V́elez, R. Weiss, M. A. Sheldon, and D. K. Gifford. Fast and effective query refinement. SIGIR Forum, 31(SI):6-15, 1997.