Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest

Yifan Jin, Jiangmeng Li, Zheng Lian, Chengbo Jiao, Xiaohui Hu


Abstract
Medical Relation Extraction (MRE) task aims to extract relations between entities in medical texts. Traditional relation extraction methods achieve impressive success by exploring the syntactic information, e.g., dependency tree. However, the quality of the 1-best dependency tree for medical texts produced by an out-of-domain parser is relatively limited so that the performance of medical relation extraction method may degenerate. To this end, we propose a method to jointly model semantic and syntactic information from medical texts based on causal explanation theory. We generate dependency forests consisting of the semantic-embedded 1-best dependency tree. Then, a task-specific causal explainer is adopted to prune the dependency forests, which are further fed into a designed graph convolutional network to learn the corresponding representation for downstream task. Empirically, the various comparisons on benchmark medical datasets demonstrate the effectiveness of our model.
Anthology ID:
2022.coling-1.216
Volume:
Proceedings of the 29th International Conference on Computational Linguistics
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
COLING
SIG:
Publisher:
International Committee on Computational Linguistics
Note:
Pages:
2450–2460
Language:
URL:
https://aclanthology.org/2022.coling-1.216
DOI:
Bibkey:
Cite (ACL):
Yifan Jin, Jiangmeng Li, Zheng Lian, Chengbo Jiao, and Xiaohui Hu. 2022. Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2450–2460, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
Cite (Informal):
Supporting Medical Relation Extraction via Causality-Pruned Semantic Dependency Forest (Jin et al., COLING 2022)
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PDF:
https://aclanthology.org/2022.coling-1.216.pdf
Code
 jyf123/CP-GCN