Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering

Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering

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by Andreas Vlachos , Anna Korhonen , Zoubin Ghahramani

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@MISC{Vlachos_unsupervisedand,
    author = {Andreas Vlachos and Anna Korhonen and Zoubin Ghahramani},
    title = {Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering},
    year = {}
}

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Abstract

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 DP-MMs 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. 1

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