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 classbased approaches, it produces humaninterpretable 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-ofthe-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).
REF proposed a generative approach to use extended LDA to model selectional preferences.
A Latent Dirichlet Allocation Method for Selectional Preferences
ACL
Computer Science