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A Hierarchical Pitman-Yor Process HMM for Unsupervised Part of Speech Induction
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@MISC{Blunsom_ahierarchical,
author = {Phil Blunsom and Trevor Cohn},
title = {A Hierarchical Pitman-Yor Process HMM for Unsupervised Part of Speech Induction},
year = {}
}
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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. 1
Keyphrases
unsupervised part hierarchical pitman-yor process hmm speech induction unsupervised part-of-speech induction principled mean lexical characteristic hierarchical pitman-yor new type-based sampling algorithm fractional table count single model several strand hierarchical pitman-yor model novel hidden markov model empirical evaluation
