A Corpus-Based Approach for Building Semantic Lexicons

Semantic knowledge can be a great asset to natural language processing systems, but it is usually hand-coded for each application. Although some semantic information is available in general-purpose knowledge bases such as WordNet and Cyc, many applications require domain-specific lexicons that represent words and categories for a particular topic. In this paper, we present a corpus-based method that can be used to build semantic lexicons for specific categories. The input to the system is a small set of seed words for a category and a representative text corpus. The output is a ranked list of words that are associated with the category. A user then reviews the top-ranked words and decides which ones should be entered in the semantic lexicon. In experiments with five categories, users typically found about 60 words per category in 10-15 minutes to build a core semantic lexicon.

• Publication year

  1997

• Venue

  Conference on Empirical Methods in Natural Language Processing

• Publication date

  1997-06-10

• Fields of study

  Computer Science

• Identifiers
  arXiv cmp-lg/9706013
• 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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