A Corpus-Based Approach for Building Semantic Lexicons

Ellen Riloff; Jessica Shepherd

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arXiv · cmp-lg/9706013

A Corpus-Based Approach for Building Semantic Lexicons

Ellen Riloff·Jessica Shepherd

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Abstract

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.

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Ellen Riloff, Jessica Shepherd. 1997-06-10. A Corpus-Based Approach for Building Semantic Lexicons. https://arxiv.org/abs/cmp-lg/9706013

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