Biomedical Event Causal Relation Extraction by Reasoning Optimal Entity Relation Path (2024)

Abstract: “Biomedical Event Causal Relation Extraction (BECRE) is an important task in biomedical infor-mation extraction. Existing methods usually use pre-trained language models to learn semanticrepresentations and then predict the event causal relation. However, these methods struggle tocapture sufficient cues in biomedical texts for predicting causal relations. In this paper, we pro-pose a Path Reasoning-based Relation-aware Network (PRRN) to explore deeper cues for causalrelations using reinforcement learning. Specifically, our model reasons the relation paths betweenentity arguments of two events, namely entity relation path, which connects the two biomedicalevents through the multi-hop interactions between entities to provide richer cues for predictingevent causal relations. In PRRN, we design a path reasoning module based on reinforcementlearning and propose a novel reward function to encourage the model to focus on the length andcontextual relevance of entity relation paths. The experimental results on two datasets suggestthat PRRN brings considerable improvements over the state-of-the-art models.Introduction”

## introduction biomedical event causal relation extraction (becre) is an important task in the field of biomedical information extraction which aims to identify the causal relation between two events in biomedical texts. as shown in fig. 1 , given the context and two events, a becre model needs to predict whether there is a causal relation between head and tail events, i.e. "phosphorylated cause ---→ degraded". becre has played an important role in many downstream tasks such as knowledge question answering @xcite , automatic decision-making, and knowledge discovery @xcite . becre is challenging because event causal relations in biomedical texts are always expressed in implicit ways. existing methods usually utilize pre-trained language models to predict event causal relations @xcite . @xcite analyzed the impact of in-domain pre-training and distillation on the performance of @xcite . @xcite used four different biomedical repositories that are publicly available to investigate the effect of the corpus used for pre-training biobert @xcite . these methods solely learned features of context relying on pre-trained language models. for further enhancing the ability of models to understand the context, @xcite introduced external knowledge to build hierarchical knowledge graphs for the contexts. however, the above methods still have difficulty in capturing sufficient cues for event causal relations, leading to limited performance in becre. to address the above issues, we pay attention to the entity relation path between biomedical events, and propose a method based on the entity relation path, named path reasoning-based relation-aware network (prrn). in prrn, the entity relation path provides rich cues for predicting event causal relations. for example, in fig. 1 , "gsk3beta catalyze -----→ p hosphorylation resultin -----→ pβ -catanin ist argetof -------→ proteasome" is a entity relation path between the event pair (phosphorylated, degraded). the entity relation path describes multi-hop interactions between the head argument, gsk3beta and the tail argument, proteasome, including that gsk3beta can catalyze phosphorylation, and pβ-catanin is the product of phosphorylation and the target of proteasome. the interactions reveal an underlying cue that β-catanin is degraded by proteasome because gsk3beta converts β-catanin to pβ-catanin through phosphorylation. so we can see that the entity relation path introduces multi-hop interactions of entities to provide rich cues for predicting event causal relation. to obtain the optimal entity relation path, we design a path reasoning module based on reinforcement learning and propose a novel reward function. in the module, a policy-based agent network gradually approaches the optimal reasoning strategy in the way of @xcite . the reward function we proposed in prrn jointly encourages the length and contextual relevance of paths. we conduct extensive experiments to evaluate the effectiveness of prrn. the experimental results show that our method outperforms previous methods. in summary, the main contributions of this paper are: ## method following the previous works @xcite , we formulate becre as a classification problem. given a sentence and an event pair, we predict whether there is a causal relation between the two events. fig. 2 schematically visualizes our approach, which mainly consists of three parts, including path reasoning module, representation module, and output module. however, in pca, brca1 can bind stat3 to promote jak and stat3 activation. however, in pca, brca1 can bind stat3 to promote jak and stat3 activation. ber network 1 2 6 5 4 3 1 2 6 5 4 3 ber network 1 2 6 5 4 3 agent softmax state r l r c 1 2 3 6 1 2 3 6 path a r s environment path reasoning module spacy context encoder context encoder path encoder path encoder representation module output module classifier classifier contextual entity event argument entity entity leaky-relu  (a|s)  (a|s)  (a|s) leaky-relu i p reward function