Chinese Part-of-Speech Tagging Method Based on Maximum Entropy Model and Hidden Markov Model

doi:10.13438/j.cnki.jdzk.2020.02.004

Journal of Jishou University（Natural Sciences Edition)

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Chinese Part-of-Speech Tagging Method Based on Maximum Entropy Model and Hidden Markov Model

ZHOU Tan, MO Liping, HU Meiqi, LI Hangcheng

(College of Information Science & Engineering, Jishou University, Jishou 416000, Hunan China)

Online:2020-03-25 Published:2020-09-08

Abstract

Abstract:

In order to further improve the efficiency of part-of-speech tagging in Chinese corpora, experiments of Chinese part-of-speech tagging methods based on the maximum entropy model (MEM) and the hidden Markov model (HMM) are designed according to the theoretical basis, algorithms, and application technology. The experimental results show that the Chinese part-of-speech tagging algorithms based on MEM and HMM have obtained a very consistent and high-coverage tagging result and the three indicators of tagging accuracy, recall rate and F1 value have reached above 92%, with the effect of MEM better than that of HMM.

Key words: maximum entropy model, hidden Markov model, Chinese part-of-speech tagging

ZHOU Tan, MO Liping, HU Meiqi, LI Hangcheng. Chinese Part-of-Speech Tagging Method Based on Maximum Entropy Model and Hidden Markov Model[J]. Journal of Jishou University（Natural Sciences Edition), DOI: 10.13438/j.cnki.jdzk.2020.02.004.

Chinese Part-of-Speech Tagging Method Based on Maximum Entropy Model and Hidden Markov Model

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