Multi-feature word embedding based named entity recognition in classical Chinese texts

Named Entity Recognition (NER) in classical Chinese is a crucial task in the natural language processing of classical texts and the construction of knowledge graphs. Its primary focus is the extraction of entities such as events, locations, and personalities from historical documents. Current mainstream NER methods predominantly utilize single-level textual features, neglecting the character structure and character-word features, which limits the acquisition of sufficient character structure and word information. Consequently, this study proposes a novel NER method for classical Chinese, integrating character and positional multi-feature vectors. Initially, a multi-feature word embedding method combining character and positional information is established. Text sequence features are then extracted using a Bidirectional Long Short-Term Memory (Bi-LSTM) network. Subsequently, a multi-head attention mechanism is employed to capture context-sensitive features across various subspaces. Finally, a Conditional Random Field (CRF) outputs the predicted annotation sequence. Experimental results on the C-CLUE dataset demonstrate that the proposed method significantly enhances NER performance compared to baseline algorithms like Bert, achieving high average F1-Scores of 77.04%, thus exhibiting high accuracy and usability.