Full-abstract biomedical relation extraction with keyword-attentive domain knowledge infusion

Xian Zhu, Lele Zhang, Jiangnan Du, Zhifeng Xiao

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


Relation extraction (RE) is an essential task in natural language processing. Given a context, RE aims to classify an entity-mention pair into a set of pre-defined relations. In the biomedical field, building an efficient and accurate RE system is critical for the construction of a domain knowledge base to support upper-level applications. Recent advances have witnessed a focus shift from sentence-to document-level RE problems, which are more challenging due to the need for inter-and intra-sentence semantic reasoning. This type of distant dependency is difficult to understand and capture for a learning algorithm. To address the challenge, prior efforts either attempted to improve the cross-sentence text representation or infuse domain or local knowledge into the model. Both strategies demonstrated efficacy on various datasets. In this paper, a keyword-attentive knowledge infusion strategy is proposed and integrated into BioBERT. A domain keyword collection mechanism is developed to discover the most relation-suggestive word tokens for bio-entities in a given context. By manipulating the attention masks, the model can be guided to focus on the semantic interaction between bio-entities linked by the keywords. We validated the proposed method on the Biocreative V Chemical Disease Relation dataset with an F1 of 75.6%, outperforming the state-of-the-art by 5.6%.

Original languageEnglish (US)
Article number7318
JournalApplied Sciences (Switzerland)
Issue number16
StatePublished - Aug 2 2021

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes


Dive into the research topics of 'Full-abstract biomedical relation extraction with keyword-attentive domain knowledge infusion'. Together they form a unique fingerprint.

Cite this