A Decision Tree of Bigrams is an Accurate Predictor of Word Sense

This paper presents a corpus-based approach to word sense disambiguation where a decision tree assigns a sense to an ambiguous word based on the bigrams that occur nearby. This approach is evaluated using the sense-tagged corpora from the 1998 SENSEVAL word sense disambiguation exercise. It is more accurate than the average results reported for 30 of 36 words, and is more accurate than the best results for 19 of 36 words.

• Publication year

  2001

• Venue

  North American Chapter of the Association for Computational Linguistics

• Publication date

  2001-03-29

• Fields of study

  Linguistics, Computer Science

• Identifiers
  DOI 10.3115/1073336.1073347arXiv cs/0103026
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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