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Related Concept Videos

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  5. Information And Computing Sciences
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  9. Natural Language Processing
  11. A Boundary Enhanced Multi-task Neural Attention Approach For Chinese Named Entity Recognition

A boundary enhanced multi-task neural attention approach for Chinese named entity recognition

Jun Pan1, Mingcheng Xiao1, Mengpei Li1

  • 1School of Science, Zhejiang University of Science and Technology, Hangzhou, 310023, China.

Scientific Reports
|November 21, 2025

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View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel joint Named Entity Recognition (NER) framework for Chinese text. By combining boundary detection with entity identification, it significantly improves Chinese NER performance.

Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Artificial Intelligence

Background:

  • Named Entity Recognition (NER) is crucial for Chinese information processing.
  • The absence of explicit word boundaries in Chinese presents unique challenges for NER tasks.

Purpose of the Study:

  • To propose a novel approach for Chinese NER by framing it as a joint task.
  • To enhance NER performance by integrating boundary detection with entity identification.

Main Methods:

  • An encoder-decoder architecture is employed, combining sequence labeling for NER and binary classification for boundary prediction.
  • Hybrid embeddings and a convolutional attention network are used for enhanced word representations and contextual information extraction.
  • Two Bi-GRU networks predict entity start/end, and a feature fusion layer integrates main and auxiliary tasks.
Keywords:
Chinese named entity recognitionConvolutional attention networkEncoder-decoderMulti-task learning

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Main Results:

  • The proposed multi-task learning framework significantly improves Chinese NER performance.
  • Experimental results on Weibo and Ontonotes5.0 datasets demonstrate superior performance compared to existing models.

Conclusions:

  • Jointly learning boundary detection and entity identification effectively addresses challenges in Chinese NER.
  • The integrated approach enhances the accuracy and robustness of Chinese NER systems.