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This chapter explores the fundamentals of Chinese word segmentation and part-of-speech tagging, emphasizing challenges and key algorithms in Chinese information processing. The discussion covers segmentation difficulties, including vocabulary selection, ambiguity resolution, and out-of-vocabulary word recognition. Advanced models, such as hierarchical hidden Markov models (HHMM) and bidirectional recurrent neural networks (Bi-RNN) combined with conditional random fields (CRF), are analyzed for their effectiveness in improving segmentation accuracy. This chapter concludes with an application demonstration of NLPIR-ICTCLAS, a leading system in Chinese lexical analysis, showing its high accuracy and adaptability in large-scale natural language processing tasks.

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Chinese Word Segmentation and Part-of-Speech Tagging

  • 章节

  • 发表日期：

    2025年

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  • Huaping Zhang,
  • Jianyun Shang

摘要

This chapter explores the fundamentals of Chinese word segmentation and part-of-speech tagging, emphasizing challenges and key algorithms in Chinese information processing. The discussion covers segmentation difficulties, including vocabulary selection, ambiguity resolution, and out-of-vocabulary word recognition. Advanced models, such as hierarchical hidden Markov models (HHMM) and bidirectional recurrent neural networks (Bi-RNN) combined with conditional random fields (CRF), are analyzed for their effectiveness in improving segmentation accuracy. This chapter concludes with an application demonstration of NLPIR-ICTCLAS, a leading system in Chinese lexical analysis, showing its high accuracy and adaptability in large-scale natural language processing tasks.