Latent variable models of concept-attribute attachment
Joseph Reisinger, MARIUS A. PAŞCA · 2009
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 Allocation and evaluate along two dimensions: (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 outperform previously proposed heuristic methods, greatly improving the specificity of attributes at each concept.