PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A Hierarchical Pitman-Yor Process HMM for Unsupervised Part of Speech Induction
Phil Blunsom and Trevor Cohn
In: ACL-HLT 2011, 19-24 Jun 2011, Portland, OR, USA.

Abstract

In this work we address the problem of unsupervised part-of-speech induction by bringing together several strands of research into a single model. We develop a novel hidden Markov model incorporating sophisticated smoothing using a hierarchical Pitman-Yor processes prior, providing an elegant and principled means of incorporating lexical characteristics. Central to our approach is a new type-based sampling algorithm for hierarchical Pitman-Yor models in which we track fractional table counts. In an empirical evaluation we show that our model consistently out-performs the current state-of-the-art across 10 languages.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Natural Language Processing
ID Code:9515
Deposited By:Trevor Cohn
Deposited On:26 April 2012