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<p>Owing to the unique composition principles of the Chinese language and the certain rules within the radical information, Chinese named entity recognition differs from English. These features prove beneficial for models tasked with identifying entity types, furnishing them with valuable cues and patterns that enhance recognition accuracy. Over the past few years, there has been an increasing trend to utilize certain pre-trained models, BERT for instance, to deal with the task of Chinese named entity recognition. However, existing models have not integrated external features into the underlying layers of BERT. Within this study, we propose a multi-feature fusion BERT model based on nonlinear spiking neural convolution models, which is called MF-BERT. It extracts features from three perspectives, namely radical information, character, and lexicon, and integrates them into the bottom layer of BERT. By introducing a nonlinear spiking neural convolution model, a radical feature extraction module, called Conv-SNP-RFE, is designed to effectively extract radical features. Moreover, using Global Pointer instead of CRF to assign labels to sentences facilitates better processing of nested entities. We carried out experiments on three commonly used datasets and contrasted them with some baseline models. The experiment results show that the proposed MF-BERT model is effective for the Chinese named entity recognition task. The code is available on:<a href="https://github.com/jiangmingtaoo/MF-BERT">https://github.com/jiangmingtaoo/MF-BERT</a>.</p>

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Multi-feature fusion method based on nonlinear spiking neural convolutional model for Chinese named entity recognition

  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Mingtao Jiang,
  • Hong Peng,
  • Jun Wang,
  • Qian Yang

摘要

Owing to the unique composition principles of the Chinese language and the certain rules within the radical information, Chinese named entity recognition differs from English. These features prove beneficial for models tasked with identifying entity types, furnishing them with valuable cues and patterns that enhance recognition accuracy. Over the past few years, there has been an increasing trend to utilize certain pre-trained models, BERT for instance, to deal with the task of Chinese named entity recognition. However, existing models have not integrated external features into the underlying layers of BERT. Within this study, we propose a multi-feature fusion BERT model based on nonlinear spiking neural convolution models, which is called MF-BERT. It extracts features from three perspectives, namely radical information, character, and lexicon, and integrates them into the bottom layer of BERT. By introducing a nonlinear spiking neural convolution model, a radical feature extraction module, called Conv-SNP-RFE, is designed to effectively extract radical features. Moreover, using Global Pointer instead of CRF to assign labels to sentences facilitates better processing of nested entities. We carried out experiments on three commonly used datasets and contrasted them with some baseline models. The experiment results show that the proposed MF-BERT model is effective for the Chinese named entity recognition task. The code is available on:https://github.com/jiangmingtaoo/MF-BERT.