Joint Learning-based Causal Relation Extraction from Biomedical Literature

TL;DR

This paper introduces a joint learning model for biomedical causal relation extraction that combines entity relation and function detection, leveraging their correlation to improve accuracy and achieve state-of-the-art results.

Contribution

The paper presents a novel joint learning approach that simultaneously models entity relations and functions, enhancing performance over separate models in biomedical text mining.

Findings

Outperforms separate models in BEL statement extraction

Achieves F1 scores of 58.4% and 37.3% on BioCreative-V corpus

Reaches state-of-the-art performance in Stage 2 evaluations

Abstract

Causal relation extraction of biomedical entities is one of the most complex tasks in biomedical text mining, which involves two kinds of information: entity relations and entity functions. One feasible approach is to take relation extraction and function detection as two independent sub-tasks. However, this separate learning method ignores the intrinsic correlation between them and leads to unsatisfactory performance. In this paper, we propose a joint learning model, which combines entity relation extraction and entity function detection to exploit their commonality and capture their inter-relationship, so as to improve the performance of biomedical causal relation extraction. Meanwhile, during the model training stage, different function types in the loss function are assigned different weights. Specifically, the penalty coefficient for negative function instances increases to effectively improve the precision of function detection. Experimental results on the BioCreative-V Track 4 corpus show that our joint learning model outperforms the separate models in BEL statement extraction, achieving the F1 scores of 58.4% and 37.3% on the test set in Stage 2 and Stage 1 evaluations, respectively. This demonstrates that our joint learning system reaches the state-of-the-art performance in Stage 2 compared with other systems.