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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.

Computational Intelligence and Neuroscience

|September 26, 2022

Summary

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.