Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering
Friday 21 November 2008 at 10.30 AM by Webmaster
Séminaire RCLN+A3 (Andreas Vlachos)
Friday 21 November 2008 at 10.30 AM
Location: LIPN,B311, LIPN — Duration: one hour
Summary: In this work we apply Dirichlet Process Mixture Models (DPMMs) to a learning task in natural language processing (NLP): lexical-semantic verb clustering. Furthermore, we propose a novel method of guiding the DPMM towards a particular clustering solution using pairwise constraints. The quantitative and qualitative evaluation performed highlights the benefits of both standard and constrained DPMMs compared to previously used approaches. Results on datasets from general English and the biomedical domain are presented.