Paper: Latent Variable Models of Concept-Attribute Attachment

ACL ID P09-1070
Title Latent Variable Models of Concept-Attribute Attachment
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors

This paper presents a set of Bayesian methods for automatically extending the WORDNET ontology with new concepts and annotating existing concepts with generic property fields, or attributes. We base our approach on Latent Dirichlet Al- location and evaluate along two dimen- sions: (1) the precision of the ranked lists of attributes, and (2) the quality of the attribute assignments to WORDNET concepts. In all cases we find that the principled LDA-based approaches outper- form previously proposed heuristic meth- ods, greatly improving the specificity of attributes at each concept.