A Maximum Entropy Tagging Model with Unsupervised Hidden Markov Models

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概要

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• We describe a new tagging model where the states of a hidden Markov model (HMM) estimated by unsupervised learning are incorporated as the features in a maximumentropy model.Our method for exploiting unsupervised learning of a probabilisticmodel can reduce the cost of building taggers with a small annotated corpus.Experimentalresults on English POS tagging and Japanese word segmentation showthat our method greatly improves the tagging accuracy when the model is trainedwith a small annotated corpus.Furthermore, our English POS tagger achieved astate-of-the-art PUS tagging accuracy (96.84%) when a large annotated corpus isavailable.

• 言語処理学会の論文

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