Paper: A Latent Dirichlet Allocation Method for Selectional Preferences

ACL ID P10-1044
Title A Latent Dirichlet Allocation Method for Selectional Preferences
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

The computation of selectional prefer- ences, the admissible argument values for a relation, is a well-known NLP task with broad applicability. We present LDA-SP, which utilizes LinkLDA (Erosheva et al., 2004) to model selectional preferences. By simultaneously inferring latent top- ics and topic distributions over relations, LDA-SP combines the benefits of pre- vious approaches: like traditional class- based approaches, it produces human- interpretable classes describing each re- lation’s preferences, but it is competitive with non-class-based methods in predic- tive power. We compare LDA-SP to several state-of- the-art methods achieving an 85% increase in recall at 0.9 precision over mutual in- formation (Erk, 2007). We also eval- uate LDA-SP’s effectiveness at filtering improper app...