Bootstrapping a Semantic Lexicon on Verb Similarities

We present a bootstrapping algorithm to create a semantic lexicon from a list of seed words and a corpus that was mined from the web. We exploit extraction patterns to bootstrap the lexicon and use collocation statistics to dynamically score new lexicon entries. Extraction patterns are subsequently scored by calculating the conditional probability in relation to a non-related text corpus. We find that verbs that are highly domain related achieved the highest accuracy and collocation statistics affect the accuracy positively and negatively during the bootstrapping runs.