A Latent Dirichlet Allocation Method for Selectional Preferences

Alan Ritter, Mausam, Oren Etzioni

Abstract:

The computation of selectional preferences, 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 topics and topic distributions over relations, LDA-SP combines the benefits of previous approaches: like traditional class-based approaches, it produces human-interpretable classes describing each relation's preferences, but it is competitive with non-class-based methods in predictive power.

We compare LDA-SP to several state-of-the-art methods achieving an 85% increase in recall at 0.9 precision over mutual information (Erk, 2007). We also evaluate LDA-SP's effectiveness at filtering improper applications of inference rules, where we show substantial improvement over Pantel et al.'s system (Pantel et al., 2007).