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Chinese POS Disambiguation and Unknown Word Guessing with Lexicalized HMMs

Chinese POS Disambiguation and Unknown Word Guessing with Lexicalized HMMs

Guohong Fu, Kang-Kwong Luke
Copyright: © 2009 |Pages: 13
ISBN13: 9781605660523|ISBN10: 1605660523|EISBN13: 9781605660530
DOI: 10.4018/978-1-87828-991-9.ch101
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MLA

Fu, Guohong, and Kang-Kwong Luke. "Chinese POS Disambiguation and Unknown Word Guessing with Lexicalized HMMs." Human Computer Interaction: Concepts, Methodologies, Tools, and Applications, edited by Chee Siang Ang and Panayiotis Zaphiris, IGI Global, 2009, pp. 1595-1607. https://doi.org/10.4018/978-1-87828-991-9.ch101

APA

Fu, G. & Luke, K. (2009). Chinese POS Disambiguation and Unknown Word Guessing with Lexicalized HMMs. In C. Ang & P. Zaphiris (Eds.), Human Computer Interaction: Concepts, Methodologies, Tools, and Applications (pp. 1595-1607). IGI Global. https://doi.org/10.4018/978-1-87828-991-9.ch101

Chicago

Fu, Guohong, and Kang-Kwong Luke. "Chinese POS Disambiguation and Unknown Word Guessing with Lexicalized HMMs." In Human Computer Interaction: Concepts, Methodologies, Tools, and Applications, edited by Chee Siang Ang and Panayiotis Zaphiris, 1595-1607. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-87828-991-9.ch101

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Abstract

This article presents a lexicalized HMM-based approach to Chinese part-of-speech (POS) disambiguation and unknown word guessing (UWG). In order to explore word-internal morphological features for Chinese POS tagging, four types of pattern tags are defined to indicate the way lexicon words are used in a segmented sentence. Such patterns are combined further with POS tags. Thus, Chinese POS disambiguation and UWG can be unified as a single task of assigning each known word to input a proper hybrid tag. Furthermore, a uniformly lexicalized HMM-based tagger also is developed to perform this task, which can incorporate both internal word-formation patterns and surrounding contextual information for Chinese POS tagging under the framework of HMMs. Experiments on the Peking University Corpus indicate that the tagging precision can be improved with efficiency by the proposed approach.

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