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Chinese Named Entity Recognition for Automobile Fault Texts Based on External Context Retrieving and Adversarial

Shuhai Wang1,2, Linfu Sun1,2

  • 1School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China.

Entropy (Basel, Switzerland)
|February 26, 2025
PubMed
Summary

This study introduces a novel Chinese named entity recognition (NER) method for automobile fault texts. The approach enhances fault diagnosis by leveraging external context retrieval and adversarial training for improved key concept identification.

Keywords:
adversarial trainingautomobile fault textbidirectional long short-term memoryinformation entropynamed entity recognition

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Automotive Engineering

Background:

  • Effective mining of key concepts in automobile fault texts is essential for accurate diagnosis.
  • Current tools lack the capability to explore latent information within these texts, hindering comprehensive understanding.

Purpose of the Study:

  • To develop a Chinese named entity recognition (NER) model tailored for automobile fault texts.
  • To enhance the understanding of fault causes and improve diagnostic capabilities through advanced text mining.

Main Methods:

  • Utilized external context retrieval via a search engine.
  • Employed Lexicon Enhanced BERT for improved text embedding representation of input sentences and external contexts.
  • Integrated an attention mechanism for fusing text and context embeddings.
  • Generated adversarial samples by perturbing fused vector representations.
  • Applied BiLSTM-CRF layer for entity labeling using fused representations and adversarial samples.

Main Results:

  • Achieved state-of-the-art results on automotive fault datasets.
  • Demonstrated superior performance in identifying key concepts within automobile fault texts.
  • Successfully improved text embedding representation through external context integration.

Conclusions:

  • The proposed Chinese NER model effectively identifies key concepts in automobile fault texts.
  • External context retrieval and adversarial training significantly enhance the performance of fault text analysis.
  • The model offers a promising solution for exploring latent information and improving automotive fault diagnosis.