Sciweavers

Share
ACL
2010

A Latent Dirichlet Allocation Method for Selectional Preferences

9 years 1 months ago
A Latent Dirichlet Allocation Method for Selectional Preferences
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).
Alan Ritter, Mausam, Oren Etzioni
Added 10 Feb 2011
Updated 10 Feb 2011
Type Journal
Year 2010
Where ACL
Authors Alan Ritter, Mausam, Oren Etzioni
Comments (0)
books