Named Entity Recognition in Field of Party Building Based on BERT-BiLSTM-CRF
When constructing a knowledge graph in the field of party building,the traditional named entity recognition(NER)methods often suffer from unclear entity boundaries and polysemy of entity terms,which lead to low recognition accuracy and effi-ciency.To address these issues,this paper proposes a BERT-BiLSTM-CRF entity recognition model that integrates tree-like probability and a domain dictionary.The model involves embedding the domain dictionary into BERT for text vectorization,uti-lizes BiLSTM to acquire contextual semantic features,and applies tree-like probability to the transition probability calculation in the CRF layer to enhance word segmentation accuracy.The experimental results on the MSRA and self-constructed corpora,compared with the baseline model,show that the proposed model achieves better performance in terms of F1-score,recall,and precision.
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