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 Word Net 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 t o build semantic lexicons for specific categories. The input t o 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. Tn experiments with five categories, users typically found about 60 words per category in 10-15 minutes to build a core semantic lexicon.