A Bootstrapping Method for Learning Semantic Lexicons using Extraction Pattern Contexts

This paper describes a bootstrapping algorithm called Basilisk that learns high-quality semantic lexicons for multiple categories. Basilisk begins with an unannotated corpus and seed words for each semantic category, which are then bootstrapped to learn new words for each category. Basilisk hypothesizes the semantic class of a word based on collective information over a large body of extraction pattern contexts. We evaluate Basilisk on six semantic categories. The semantic lexicons produced by Basilisk have higher precision than those produced by previous techniques, with several categories showing substantial improvement.

• Publication year

  2002

• Venue

  Conference on Empirical Methods in Natural Language Processing

• Publication date

  2002-07-06

• Fields of study

  Computer Science

• Identifiers
  DOI 10.3115/1118693.1118721
• 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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