Abstract

VerbNet (VN) is a major large-scale English verb lexicon. Mapping verb instances to their VN classes has been proven useful for several NLP tasks. However, verbs are polysemous with respect to their VN classes. We introduce a novel supervised learning model for mapping verb instances to VN classes, using rich syntactic features and class membership constraints. We evaluate the algorithm in both in-domain and corpus adaptation scenarios. In both cases, we use the manually tagged Semlink WSJ corpus as training data. For indomain (testing on Semlink WSJ data), we achieve 95.9 % accuracy, 35.1 % error reduction (ER) over a strong baseline. For adaptation, we test on the GENIA corpus and achieve 72.4 % accuracy with 10.7% ER. This is the first large-scale experimentation with automatic algorithms for this task. 1

Citations