Selective Sampling for Example-based Word Sense Disambiguation

Atsushi Fujii, Takenobu Tokunaga, Kentaro Inui, Hozumi Tanaka

Research output: Contribution to journalArticlepeer-review

57 Citations (Scopus)

Abstract

This paper proposes an efficient example sampling method for example-based word sense disambiguation systems. To construct a database of practical size, a considerable overhead for manual sense disambiguation (overhead for supervision) is required. In addition, the time complexity of searching a large-sized database poses a considerable problem (overhead for search). To counter these problems, our method selectively samples a smaller-sized effective subset from a given example set for use in word sense disambiguation. Our method is characterized by the reliance on the notion of training utility: the degree to which each example is informative for future example sampling when used for the training of the system. The system progressively collects examples by selecting those with greatest utility. The paper reports the effectiveness of our method through experiments on about one thousand sentences. Compared to experiments with other example sampling methods, our method reduced both the overhead for supervision and the overhead for search, without the degeneration of the performance of the system.

Original languageEnglish
Pages (from-to)573-597
Number of pages25
JournalComputational Linguistics
Volume24
Issue number4
Publication statusPublished - 1998 Dec 1
Externally publishedYes

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language
  • Computer Science Applications
  • Artificial Intelligence

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