Mr. Peng Wang Profile

Peng Wang

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Proceedings Article | 27 March 2024 Paper

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

Biao Liu, Ningjia Qiu, Peng Wang

Proceedings Volume 13105, 131052Y (2024) https://doi.org/10.1117/12.3026384

KEYWORDS: Semantics, Performance modeling, Feature extraction, Education and training, Data modeling, Ablation, Modeling, Machine learning, Head, Detection and tracking algorithms

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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.

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