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Research on the Chinese Named-Entity-Relation-Extraction Method for Crop Diseases Based on BERT

文献类型： 外文期刊

作者： Zhang, Wenhao 1 ; Wang, Chunshan 1 ; Wu, Huarui 2 ; Zhao, Chunjiang 2 ; Teng, Guifa 1 ; Huang, Sufang 4 ; Liu, Zhen 4 ;

作者机构： 1.Hebei Agr Univ, Sch Informat Sci & Technol, Baoding 071001, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China

3.Hebei Key Lab Agr Big Data, Baoding 071001, Peoples R China

4.Cangzhou Acad Agr & Forestry Sci, Cangzhou 061000, Peoples R China

关键词： knowledge graph; crop disease; named-entity recognition; relation extraction; BERT; PERT

期刊名称：AGRONOMY-BASEL （ 影响因子：3.949； 五年影响因子：4.117 ）

ISSN：

年卷期： 2022 年 12 卷 9 期

页码：

收录情况： SCI

摘要： In order to integrate fragmented text data of crop disease knowledge to solve the current problems of disordered knowledge management, weak correlation and difficulty in knowledge sharing, a Chinese named-entity-relation-extraction model for crop diseases (BBCPF) was proposed in this paper by utilizing the advantage of knowledge graph in describing complex relations between disease entities in a structured form. This model was composed of two parts, i.e., named-entity recognition and relation extraction, in the form of an assembly line. To deal with the different meanings of Chinese crop disease terms in different contexts and to better obtain the contextual information, the BERT model was introduced for dynamic vector representations. Then, the BiLSTM layer was used to learn long-distance text information, and the CRF was applied to obtain the globally optimal labeling sequence, so as to output the crop disease entities. According to the entity category, the entities were divided as subjects and objects, which were then input into the disordered language model PERT to extract the contextual features of the relation data. At last, the fully connected layer was used to decode the information and output the crop disease entity-relation triples. The experiment results show that, on the self-built disease corpus dataset, the Precision, Recall, and Fl-Score values of the established model reached 85.63%, 79.46% and 82.43%, respectively, for entity extraction, and reached 97.96%, 98.43% and 98.16%, respectively, for relation extraction. This paper provides an effective method for information extraction in the construction of Chinese crop disease domain knowledge graphs.

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