B-LBConA: a medical entity disambiguation model based on Bio-LinkBERT and context-aware mechanism.

Journal: BMC bioinformatics
Published Date:

Abstract

BACKGROUND: The main task of medical entity disambiguation is to link mentions, such as diseases, drugs, or complications, to standard entities in the target knowledge base. To our knowledge, models based on Bidirectional Encoder Representations from Transformers (BERT) have achieved good results in this task. Unfortunately, these models only consider text in the current document, fail to capture dependencies with other documents, and lack sufficient mining of hidden information in contextual texts.

Authors

  • Siyu Yang
    Heilongjiang University of Chinese Medicine, 24 Heping Road, Xiangfang District, Harbin, China 8615-0040.
  • Peiliang Zhang
    Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian 116622, China.
  • Chao Che
    Key Laboratory of Advanced Design and Intelligent Computing (Dalian University), Ministry of Education, Dalian 116622, China.
  • Zhaoqian Zhong
    Key Laboratory of Advanced Design and Intelligent Computing, Ministry of Education, Dalian University, 116622, Dalian, China. zhaoqianzhong@gmail.com.