Selective Sampling for Example-based Word Sense Disambiguation

Atsushi Fujii, Takenobu Tokunaga, Kentaro Inui, Hozumi Tanaka

研究成果: Article査読

69 被引用数 (Scopus)


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.

ジャーナルComputational Linguistics
出版ステータスPublished - 1998 12月

ASJC Scopus subject areas

  • 言語および言語学
  • 言語学および言語
  • コンピュータ サイエンスの応用
  • 人工知能


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