An Entity Recognition Model Based on Deep Learning Fusion of Text Feature. Issue 2 (March 2022)

• Record Type: Journal Article
• Authors: Shang, Fengjun; Ran, Chunfu
• Is Part Of: Information processing & management. Volume 59:Issue 2(2022)
• Publication Date: 2022-03
• Subjects: Deep learning -- Text features -- Knowledge graph -- Entity recognition -- Relationship extraction; Information storage and retrieval systems -- Periodicals; Information science -- Periodicals; Systèmes d'information -- Périodiques; Sciences de l'information -- Périodiques; Information science; Information storage and retrieval systems; Periodicals; 658.4038
• DOI: 10.1016/j.ipm.2021.102841
• Languages: English
• ISSNs: 0306-4573

Abstract

Highlights: A multi-feature entity recognition model is designed. A multi-feature word embedding algorithm is proposed, which integrates pinyin, radical and the meaning of the character itself, so that the word embedding vector has the characteristics of Chinese characters and the characteristics of diabetes text. In modeling, CNN and BiLSTM are used to extract the local and global features before and after the text sequence respectively, which solved the problem that the traditional method can not capture the dependence before and after the text sequence. CRF is used to output the predicted tag sequence. Abstract: Nowadays a large amount of knowledge has been born on the Internet and the way of constructing knowledge graph is not uniform. Due to the recent outbreak of numerous diseases, the community has placed more importance on the healthcare system. Diabetes is a severe disease that affect people's health. To assist the health sector in combating this deadly disease, the authors developed a deep learning strategy for diabetes named entity extraction based on a fusion of text characteristic and relationship extraction utilizing text data as the object. This study aims to develop a multi-feature entity recognition model that considers the differences in text features across different fields. Firstly, in the word embedding layer, a multi-feature word embedding algorithm is proposed, which integrates Pinyin, radical, and the meaning of the character itself, so that the word embedding vector has the characteristics of Chinese characters and diabetes text. Then in modeling, CNN and BiLSTM are used to extract the local and global features before and after the text sequence, respectively, which solved the problem that the traditional method cannot capture the dependence before and after the text sequence. Finally, CRF is used to output the predicted tag sequence. The experimental results show that the multi-feature embedding algorithm and local features extracted by CNN can effectively improve the recognition effect of the entity recognition model.