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Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering| old_uid | 5657 |
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| title | Unsupervised and Constrained Dirichlet Process Mixture Models for Verb Clustering |
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| start_date | 2008/11/21 |
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| schedule | 10h30 |
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| online | no |
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| 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. |
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| oncancel | Séance initialement prévue le 20/11 |
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| responsibles | Poibeau |
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