Paper: Discriminative Learning of Selectional Preference from Unlabeled Text

ACL ID D08-1007
Title Discriminative Learning of Selectional Preference from Unlabeled Text
Venue Conference on Empirical Methods in Natural Language Processing
Session Main Conference
Year 2008
Authors

We present a discriminative method for learn- ing selectional preferences from unlabeled text. Positive examples are taken from ob- served predicate-argument pairs, while nega- tives are constructed from unobserved combi- nations. We train a Support Vector Machine classifier to distinguish the positive from the negative instances. We show how to parti- tion the examples for efficient training with 57 thousand features and 6.5 million training instances. The model outperforms other re- cent approaches, achieving excellent correla- tion with human plausibility judgments. Com- pared to Mutual Information, it identifies 66% more verb-object pairs in unseen text, and re- solves 37% more pronouns correctly in a pro- noun resolution experiment.