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

Zhiliang Guo; Alla Solianyk

doi:10.1117/12.3026327

27 March 2024 Multi-feature word embedding based named entity recognition in classical Chinese texts

Zhiliang Guo, Alla Solianyk

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131052D (2024) https://doi.org/10.1117/12.3026327
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

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 Its primary focus is the extraction of entities such as events, locations, and personalities from historical documents.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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhiliang Guo and Alla Solianyk "Multi-feature word embedding based named entity recognition in classical Chinese texts", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131052D (27 March 2024); https://doi.org/10.1117/12.3026327

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KEYWORDS

Semantics

Head

Detection and tracking algorithms

Analytical research

Cultural heritage

Data modeling

Deep learning

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