Computer Science

Computer Science ›› 2023, Vol. 50 ›› Issue (10): 223-229.doi: 10.11896/jsjkx.220900108

• Artificial Intelligence • Previous Articles     Next Articles

Biomedical Relationship Extraction Method Based on Prompt Learning

WEN Kunjian, CHEN Yanping, HUANG Ruizhang, QIN Yongbin   

  1. State Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China
    College of Computer Science and Technology,Guizhou University,Guiyang 550025,China
  • Received:2022-09-12 Revised:2022-12-07 Online:2023-10-10 Published:2023-10-10
  • About author:WEN Kunjian, born in 1998, postgra-duate．His main research interests include biological information extraction and so on. CHEN Yanping, born in 1980，Ph. D，associate professor.His main research interests include artificial intelligence and natural language processing.
  • Supported by:
    National Natural Science Foundation of China(62166007).

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Abstract: Extracting the relationship between entities from unstructured biomedical text data is of great significance for the development of biomedical informatization.At the same time,it is also a research hotspot in the field of natural language processing.At present,there are two difficulties in correctly extracting the relationship between entities in biomedical data.One is that in biomedicine,entity words are mostly composed of compound words and unknown words,which makes it difficult for the model to learn the semantic features inside the entity.Second,because there are few biomedical band labeling data and the amount of parameters of neural network is large,the neural network is prone to overfitting.Therefore,a biomedical relationship extraction method based on prompt learning is proposed in this paper.In this paper,an annotation label for entities is added to prompt entities to enhance entity semantics and contact context information.In addition,based on the traditional prompt optimization me-thod,this paper uses the continuity template to alleviate the performance deviation caused by the manual design of the template.At the same time,combined with the depth prefix to control the depth prompt ability of attention,the model can still achieve good results when dealing with a small amount of data.

Key words: Relation extraction, Biological information extraction, Prompt-tuning

CLC Number: 

  • TP391
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