Chinese named entity recognition with fusion of attention and whole word masking

Biao Liu; Ningjia Qiu; Peng Wang

doi:10.1117/12.3026384

27 March 2024 Chinese named entity recognition with fusion of attention and whole word masking

Biao Liu, Ningjia Qiu, Peng Wang

Proceedings Volume 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023); 131052Y (2024) https://doi.org/10.1117/12.3026384
Event: 3rd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 2023, Qingdao, China

Abstract

In order to effectively obtain word-level semantic knowledge and address the issue of inaccurate extraction of salient information during feature extraction, this paper proposes a named entity recognition method that combines the RoBERTa-WWM (A Robustly Optimized BERT Pre-training Approach-Whole Word Masking) pretrained model with attention mechanisms. Firstly, the RoBERTa-WWM model is trained to obtain word-level semantic knowledge representation. This semantic representation is then sequentially inputted into a bidirectional long short-term memory (BiLSTM) network, where attention mechanisms are applied to assign weights to key information. Finally, a conditional random field (CRF) is used to obtain the globally optimal labels. Experimental results demonstrate that compared to traditional named entity algorithms that use character-level semantic knowledge, the proposed method achieves a 1.25% improvement in the F1 score. Moreover, further improvement of 2.06% in F1 score is achieved by weighting the key information, indicating the strong performance of the proposed model in Chinese named entity recognition task.

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

Citation Download Citation

Biao Liu, Ningjia Qiu, and Peng Wang "Chinese named entity recognition with fusion of attention and whole word masking", Proc. SPIE 13105, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023), 131052Y (27 March 2024); https://doi.org/10.1117/12.3026384

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KEYWORDS

Semantics

Performance modeling

Feature extraction

Education and training

Data modeling

Ablation

Detection and tracking algorithms

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