Event Causal Relation Extraction in Brain Connectomics: A Model Utilizing Weighted Joint Constrained Learning

Brain science research has entered the era of connectomics, characterized by a significant increase in published articles investigating brain structure and functional connections. Automatically and accurately extracting scientific evidence from these articles has become an urgent concern. Unlike early brain mechanism studies at the functional area level, brain connectomics studies feature more intricate experimental designs and yield complex findings. Traditional neuroimaging text mining techniques, operating at the term level, are insufficient for effectively extracting scientific evidence from brain connectomics articles. This paper addresses a key challenge in event-level neuroimaging text mining, i.e., event causal relation extraction in brain connectomics. We introduce a novel model named Brain Connectomics Event Relation Miner (BCERM), leveraging weighted joint constrained learning. By integrating a bidirectional long short-term memory (BiLSTM) network with a multi-layer perceptron (MLP), we develop a lightweight model for jointly extracting multiple event causal relations from brain connectomics articles. Given the scarcity of annotated brain connectomics corpora, we propose a weighted joint constrained learning framework. This framework integrates double consistency constraints, encompassing common sense and domain constraints, and combines them with adaptive weight learning to enhance the model's few-shot learning capability. Experimental evaluations on a real brain connectomics article dataset demonstrate that our method achieves an F-score of 70%, outperforming state-of-the-art event relation extraction methods in the low-resource environment.