4.6 Article

A joint model for entity and relation extraction based on BERT

期刊

NEURAL COMPUTING & APPLICATIONS
卷 34, 期 5, 页码 3471-3481

出版社

SPRINGER LONDON LTD
DOI: 10.1007/s00521-021-05815-z

关键词

Agricultural knowledge graph; Named entity recognition; Relation extraction; Joint extraction; BERT

资金

  1. Natural Science Foundation of Hunan Province of China [2019JJ50239]
  2. Scientific Research Fund of Hunan Provincial Education Department of China [20A249]
  3. Key Research and Development Program of Hunan Province of China [2020NK2033]

向作者/读者索取更多资源

The study introduces a construction technology for agricultural knowledge graphs, including a BERT-based entity relationship joint extraction model for extracting relationships between entities in the agricultural domain.
In recent years, as the knowledge graph has attained significant achievements in many specific fields, which has become one of the core driving forces for the development of the internet and artificial intelligence. However, there is no mature knowledge graph in the field of agriculture, so it is a great significance study on the construction technology of agricultural knowledge graph. Named entity recognition and relation extraction are key steps in the construction of knowledge graph. In this paper, based on the joint extraction model LSTM-LSTM-Bias brought in BERT pre-training language model to proposed a agricultural entity relationship joint extraction model BERT-BILSTM-LSTM which is applied to the standard data set NYT and self-built agricultural data set AgriRelation. Experimental results showed that the model can effectively extracted the relationship between agricultural entities and entities.

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