This paper describes a bootstrapping algorithm called Basilisk that learns highquality 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.
REF use a bootstrapping algorithm to learn semantic lexicons of nouns for six semantic categories, one of which is EVENTS.
A Bootstrapping Method For Learning Semantic Lexicons Using Extraction Pattern Contexts
Conference On Empirical Methods In Natural Language Processing
Computer Science