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2007 NeurIPS NeurIPS 2007

A Bayesian LDA-based model for semi-supervised part-of-speech tagging

Abstract

We present a novel Bayesian model for semi-supervised part-of-speech tagging. Our model extends the Latent Dirichlet Allocation model and incorporates the intuition that words’ distributions over tags, p(t|w), are sparse. In addition we in- troduce a model for determining the set of possible tags of a word which captures important dependencies in the ambiguity classes of words. Our model outper- forms the best previously proposed model for this task on a standard dataset.

🌱 Topic Pioneer - Part-of-Speech Tagging

🌉 Interdisciplinary Bridge - Artificial Intelligence and Natural Language Processing

📈 Trend Setter - Semi-Supervised Learning

🐣 Hot Topic Early Bird - part-of-speech tagging

Authors

Kristina Toutanova, Mark Johnson

Topics

Artificial Intelligence > Bayesian & Probabilistic > Bayesian Learning Machine Learning > Learning Types > Semi-Supervised Learning Machine Learning > Optimization & Theory > Bayesian Inference Natural Language Processing > Understanding > Part-of-Speech Tagging Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling Artificial Intelligence > Bayesian & Probabilistic > Bayesian Inference Artificial Intelligence > Learning Paradigms > Semi-Supervised Learning

Keywords

semi-supervised learning probabilistic modeling bayesian learning bayesian inference latent dirichlet allocation part-of-speech tagging bayesian model sparse distribution topic model bayesian lda ambiguity class

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