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Entity Identification of Diabetes Medical Program Based on Word Meaning Information

Zhao Weihang; Yao Quanzhu; Ding Zili; Yu Changqing; An Haonan
Given the complexity and ambiguity of solid words in diabetes mellitus cases, and the problem that static word frequency, as a weight, can-not learn enough about the influence of word importance on feature value in the current study of entity recognition, a new entity recognition model of diabetes mellitus domain is proposed which incorporates external words in embedded layer and uses attention mechanism and Multi-Head attention mechanisms. First, the embedded layer is composed of the vector features of the external lexicon trained by the Soft Lexicon model and processed by Multi-Head attention mechanisms and attention mechanisms and the character vector features generated by the BERT (Bidirectional Encoder Representation from Transformers) pre-trained language model. Then, the BiLSTM (Bi-directed Long Short-Term Memory) model is used to obtain the before and after dependenci- -es of text and extract feature information. Finally, the characters of the sequence text are sorted through the CRF (Constitutional Random Field) layer. Comparing experimental results on two different datasets, the solid model showed a 4.13% and 0.71% improvement in F1 values, respectively, compared to the original BERT-BiLSTM-CRF model, resulting in better entity recognition. F1 improved by 1.02% and 0.38%, compared with the BERT model.
Select Volume / Issues:
Year:
2023
Type of Publication:
Article
Keywords:
Naming Entity Identification; Diabetes; Multi-Head Attention; Attention Mechanisms
Journal:
IJECCE
Volume:
14
Number:
4
Pages:
26-35
Month:
July
ISSN:
2249-071X
Hits: 180

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