Document-level biomedical relation extraction via hierarchical tree graph and relation segmentation module.

ABSTRACT:

Motivation

Biomedical relation extraction at the document level (Bio-DocRE) involves extracting relation instances from biomedical texts that span multiple sentences, often containing various entity concepts such as genes, diseases, chemicals, variants, etc. Currently, this task is usually implemented based on graphs or transformers. However, most work directly models entity features to relation prediction, ignoring the effectiveness of entity pair information as an intermediate state for relation prediction. In this article, we decouple this task into a three-stage process to capture sufficient information for improving relation prediction.

Results

We propose an innovative framework HTGRS for Bio-DocRE, which constructs a hierarchical tree graph (HTG) to integrate key infor

SUBMITTER: Yuan J 

PROVIDER: S-EPMC11629692 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature