@inproceedings{lishuang-etal-2024-biomedical,
title = "Biomedical Event Causal Relation Extraction by Reasoning Optimal Entity Relation Path",
author = "Lishuang, Li and
Liteng, Mi and
Beibei, Zhang and
Yi, Xiang and
Yubo, Feng and
Xueyang, Qin and
Jingyao, Tang",
editor = "Sun, Maosong and
Liang, Jiye and
Han, Xianpei and
Liu, Zhiyuan and
He, Yulan",
booktitle = "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)",
month = jul,
year = "2024",
address = "Taiyuan, China",
publisher = "Chinese Information Processing Society of China",
url = "https://aclanthology.org/2024.ccl-1.84/",
pages = "1087--1098",
language = "eng",
abstract = "{\textquotedblleft}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{\textquotedblright}"
}
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<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”</abstract>
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%0 Conference Proceedings
%T Biomedical Event Causal Relation Extraction by Reasoning Optimal Entity Relation Path
%A Lishuang, Li
%A Liteng, Mi
%A Beibei, Zhang
%A Yi, Xiang
%A Yubo, Feng
%A Xueyang, Qin
%A Jingyao, Tang
%Y Sun, Maosong
%Y Liang, Jiye
%Y Han, Xianpei
%Y Liu, Zhiyuan
%Y He, Yulan
%S Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)
%D 2024
%8 July
%I Chinese Information Processing Society of China
%C Taiyuan, China
%G eng
%F lishuang-etal-2024-biomedical
%X “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”
%U https://aclanthology.org/2024.ccl-1.84/
%P 1087-1098
Markdown (Informal)
[Biomedical Event Causal Relation Extraction by Reasoning Optimal Entity Relation Path](https://aclanthology.org/2024.ccl-1.84/) (Lishuang et al., CCL 2024)
ACL