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A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging.

Qing Xu1, Zhiyou Wang2

  • 1Changsha University of Science and Technology, Changsha, Hunan 410000, China.

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|September 26, 2022
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This summary is machine-generated.

This study introduces a novel automated model for Chinese word segmentation and POS tagging using deep learning. The model significantly enhances accuracy and efficiency in processing Chinese text.

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Deep Learning

Background:

  • Traditional Chinese word segmentation and POS tagging rely on lexicon-based and rule-based methods.
  • These methods often fall short in achieving high label prediction accuracy for practical applications.
  • Deep learning offers a promising avenue for automated solutions in NLP tasks.

Purpose of the Study:

  • To propose a data-driven automated model for Chinese word segmentation and POS tagging.
  • To enhance the accuracy and operational efficiency of Chinese NLP tasks.
  • To address the limitations of traditional manual segmentation and tagging approaches.

Main Methods:

  • Developed a novel deep learning framework for automated Chinese word segmentation and POS tagging.
  • Implemented word2Vec for distributed word vector representation of text features.
  • Utilized an improved AlexNet with an attention mechanism for efficient long-range word encoding.
  • Incorporated an auxiliary loss function to optimize Chinese text based on frequency.

Main Results:

  • The proposed model demonstrated significant improvements in accuracy for Chinese word segmentation and POS tagging.
  • Experimental results indicated enhanced operational efficiency compared to existing models.
  • The model's effectiveness and advancement in automated Chinese NLP were validated.

Conclusions:

  • The data-driven automated model effectively addresses challenges in Chinese word segmentation and POS tagging.
  • The integration of word2Vec, improved AlexNet, attention mechanism, and auxiliary loss function yields superior performance.
  • This research advances the field of automated Chinese natural language processing.