A Part-of-Speech Tagging Model Employing Word Clustering and Syntactic Parsing

作     者：Lichi Yuan 

作者机构：School of Information Technology Jiangxi University of Finance and Economics Nanchang China Jiangxi Key Laboratory of Data and Knowledge Engineering Jiangxi University of Finance and Economics Nanchang China 

出 版 物：《Chinese Journal of Electronics》 

年 卷 期：2025年第23卷第1期

页      面：109-114页

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：National Natural Science Foundation of China Natural Science Foundation of Jiangxi Province 

摘      要：Part-Of-Speech tagging is a basic task in the field of natural language processing. This paper builds a POS tagger based on improved Hidden Markov model, by employing word clustering and syntactic parsing model. Firstly, In order to overcome the defects of the classical HMM, Markov family model (MFM), a new statistical model was introduced. Secondly, to solve the problem of data sparseness, we propose a bottom-to-up hierarchical word clustering algorithm. Then we combine syntactic parsing with part-of-speech tagging. The Part-of-Speech tagging experiments show that the improved Part-Of-Speech tagging model has higher performance than Hidden Markov models (HMMs) under the same testing conditions, the precision is enhanced from 94.642% to 97.235%.