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Communication Dans Un Congrès Année : 2013

Hidden Markov tree models for semantic class induction

Résumé

In this paper, we propose a new method for semantic class induction. First, we introduce a generative model of sentences, based on dependency trees and which takes into account homonymy. Our model can thus be seen as a generalization of Brown clustering. Second, we describe an efficient algorithm to perform inference and learning in this model. Third, we apply our proposed method on two large datasets ($10^8$ tokens, $10^5$ words types), and demonstrate that classes induced by our algorithm improve performance over Brown clustering on the task of semi-supervised supersense tagging and named entity recognition.
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Dates et versions

hal-00833288 , version 1 (12-06-2013)

Identifiants

  • HAL Id : hal-00833288 , version 1

Citer

Edouard Grave, Guillaume Obozinski, Francis Bach. Hidden Markov tree models for semantic class induction. CoNLL - Seventeenth Conference on Computational Natural Language Learning, Aug 2013, Sofia, Bulgaria. ⟨hal-00833288⟩
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