A Maximum Entropy Model for Part-Of-Speech Tagging

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A Maximum Entropy Model for Part-Of-Speech Tagging (1996)

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by Adwait Ratnaparkhi

Citations:

575 - 1 self

BibTeX

@MISC{Ratnaparkhi96amaximum,
    author = {Adwait Ratnaparkhi},
    title = {A Maximum Entropy Model for Part-Of-Speech Tagging},
    year = {1996}
}

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Abstract

This paper presents a statistical model which trains from a corpus annotated with Part-OfSpeech tags and assigns them to previously unseen text with state-of-the-art accuracy(96.6%). The model can be classified as a Maximum Entropy model and simultaneously uses many contextual "features" to predict the POS tag. Furthermore, this paper demonstrates the use of specialized features to model difficult tagging decisions, discusses the corpus consistency problems discovered during the implementation of these features, and proposes a training strategy that mitigates these problems.

Keyphrases

maximum entropy model part-of-speech tagging specialized feature corpus consistency problem many contextual feature statistical model training strategy unseen text state-of-the-art accuracy po tag part-ofspeech tag

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