Although diathesis alternations have been used as features for manual verb classification, and there is recent work on incorporating such features in computational models of human language acquisition, work on large scale verb classification has yet to examine the potential for using diathesis alternations as input features to the clustering process. This paper proposes a method for approximating diathesis alternation behaviour in corpus data and shows, using a state-of-the-art verb clustering system, that features based on alternation approximation outperform those based on independent subcategorization frames. Our alternation-based approach is particularly adept at leveraging information from less frequent data.
REF present an unsupervised approach to the semantic classification of verbs that uses approximations of diathesis alternations as features.
Diathesis alternation approximation for verb clustering
ACL
Computer Science