MFE-Transformer: Adaptive English Text Named Entity Recognition Method Based on Multi-feature Extraction and Transformer

Liuxin Gao

Computer Science and Information Systems, Tome 21 (2024) no. 4.

Résumé

English text named entity recognition aims to alleviate the problem of insufficient labeling data in the target domain. Existing methods usually use feature representation or model parameter sharing to realize cross-domain transfer of entity recognition capability, but there is still a lack of full utilization of structured knowledge in text sequences. Therefore, this paper proposes an adaptive English named text entity recognition method based on multi-feature extraction and transformer. Firstly, a bidirectional long term memory conditional random field entity recognition model based on BERT pre-trained language model is constructed on a generic domain dataset. In the training process, the weights of two character vectors of text words are dynamically calculated and combined, which makes the model make full use of the information in the character granularity, and the parts-of-speech information and block analysis are added as additional features. The word vectors, character-level features and additional features are spliced into the BiLSTM-CRF neural network model for training. Finally, experiments are carried out on five English datasets and specific cross-domain named entity recognition datasets respectively. The results show that the average performance of the proposed model is improved by 0.43% and 1.47% compared with the current cross-domain model, indicating that the structured knowledge in feature representation can effectively improve the entity recognition capability of the target domain.

Détail

Keywords: Adaptive English named text entity recognition, multi-feature extraction, transformer, domain invariant knowledge