Unsupervised word sense disambiguation using WordNet relatives

Hee Cheol Seo, Hoojung Chung, Hae Chang Rim, Sung Hyon Myaeng, Soo Hong Kim

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


This paper describes a sense disambiguation method for a polysemous target noun using the context words surrounding the target noun and its WordNet relatives, such as synonyms, hypernyms and hyponyms. The result of sense disambiguation is a relative that can substitute for that target noun in a context. The selection is made based on co-occurrence frequency between candidate relatives and each word in the context. Since the co-occurrence frequency is obtainable from a raw corpus, the method is considered to be an unsupervised learning algorithm and therefore does not require a sense-tagged corpus. In a series of experiments using SemCor and the corpus of SENSEVAL-2 lexical sample task, all in English, and using some Korean data, the proposed method was shown to be very promising. In particular, its performance was superior to that of the other approaches evaluated on the same test corpora.

Original languageEnglish
Pages (from-to)253-273
Number of pages21
JournalComputer Speech and Language
Issue number3 SPEC. ISS.
Publication statusPublished - 2004 Jul

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Human-Computer Interaction


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