PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Unsupervised and constrained dirichlet process mixture models for verb clustering
Andreas Vlachos, Anna Korhonen and Zoubin Ghahramani
In: EACL 2009, 31 MAR 2009, Athens, Greece.


In this work, we apply Dirichlet Process Mixture Models (DPMMs) to a learning task in natural language processing (NLP): lexical-semantic verb clustering. We thoroughly evaluate a method of guiding DPMMs 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. In addition, it sheds light on the use of evaluation measures and their practical application

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Information Retrieval & Textual Information Access
ID Code:6249
Deposited By:Zoubin Ghahramani
Deposited On:08 March 2010