Semantic WordNet-Based Feature Selection

  • Florentina T. HristeaEmail author
Part of the SpringerBriefs in Statistics book series (BRIEFSSTATIST)


The feature selection method we are presenting in this chapter makes use of the semantic network WordNet as knowledge source for feature selection. The method makes ample use of the WordNet semantic relations which are typical of each part of speech, thus placing the disambiguation process at the border between unsupervised and knowledge-based techniques. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will be compared to previously existing disambiguation results, obtained when performing a completely different type of feature selection. Our main conclusion will be that the Naïve Bayes model reacts well in the presence of semantic knowledge provided by WN-based feature selection when acting as clustering technique for unsupervised WSD.


Bayesian classification Word sense disambiguation  Unsupervised disambiguation Knowledge-based disambiguation  WordNet 


  1. Banerjee, S., Pedersen, T.: An adapted Lesk algorithm for word sense disambiguation using WordNet. In: Proceedings of the Third International Conference on Intelligent Text Processing and Computational Linguistics, pp. 136–145 Mexico City (2002)Google Scholar
  2. Banerjee, S., Pedersen, T.: Extended gloss overlaps as a measure of semantic relatedness. In: Proceedings of the 18th International Joint Conference on Artificial Intelligence, PP.805–810 Acapulco, Mexico (2003)Google Scholar
  3. Bruce, R., Wiebe, J., Pedersen, T.: The measure of a model. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 101–112 Philadelphia, PA (1996)Google Scholar
  4. Collins, A.H., Quillian, M.R.: Retrieval time from semantic memory. J. Verb. Learn. Verb. Be. 8, 240–247 (1969)CrossRefGoogle Scholar
  5. Fellbaum, C. (ed.): WordNet: An Electronic Lexical Database. The MIT Press, Cambridge (1998)Google Scholar
  6. Hristea, F.: Recent advances concerning the usage of the Naïve Bayes model in unsupervised word sense disambiguation. Int. Rev. Comput. Softw. 4(1), 58–67 (2009)Google Scholar
  7. Hristea, F., Popescu, M., Dumitrescu, M.: Performing word sense disambiguation at the border between unsupervised and knowledge-based techniques. Artif. Intell. Rev. 30(1), 67–86 (2008)CrossRefGoogle Scholar
  8. Hristea, F., Popescu, M.: Adjective sense disambiguation at the border between unsupervised and knowledge-based techniques. Fundam. Inf. 91(3–4), 547–562 (2009)Google Scholar
  9. Kay, M.: The concrete lexicon and the abstract dictionary. In: Proceedings of the Fifth Annual Conference of the UW Centre for the New Oxford English Dictionary, pp. 35–41 (1989)Google Scholar
  10. Leacock, C., Towell, G., Voorhees, E.: Corpus-based statistical sense resolution. In: Proceedings of the ARPA Workshop on Human Language Technology, Princeton, pp. 260–265 New Jersey (1993)Google Scholar
  11. Miller, G.A.: Nouns in WordNet: a lexical inheritance system. Int. J. Lexicogr. 3(4), 245–264 (1990)CrossRefGoogle Scholar
  12. Miller, G.A.: WordNet: a lexical database. Commun. ACM 38(11), 39–41 (1995)CrossRefGoogle Scholar
  13. Miller, G.A.: Nouns in WordNet. In: Fellbaum, C. (ed.) WordNet: An Electronic Lexical Database, pp. 23–46. The MIT Press, Cambridge (1998)Google Scholar
  14. Miller, G.A., Beckwith, R., Fellbaum, C., Gross, D., Miller, K.: WordNet: an on-line lexical database. J. Lexicogr. 3(4), 234–244 (1990)Google Scholar
  15. Miller, G.A., Hristea, F.: WordNet nouns: classes and instances. Comput. Linguist. 32(1), 1–3 (2006)CrossRefGoogle Scholar
  16. Pedersen, T., Bruce, R.: Knowledge lean word-sense disambiguation. In: Proceedings of the 15th National Conference on Artificial Intelligence, pp. 800–805 Madison, Wisconsin (1998)Google Scholar

Copyright information

© The Author(s) 2013

Authors and Affiliations

  1. 1.Faculty of Mathematics and Computer Science, Department of Computer ScienceUniversity of BucharestBucharestRomania

Personalised recommendations