Abstract
Document-level biomedical relation extraction (Bio-DocuRE) is an important branch of biomedical text mining that aims to automatically extract all relation facts from the biomedical text. Since there are a considerable number of relations in biomedical documents that need to be judged by other existing relations, logical reasoning has become a research hotspot in the past two years. However, current models with reasoning are single-granularity only based on one element information, ignoring the complementary fact of different granularity reasoning information. In addition, obtaining rich document information is a prerequisite for logical reasoning, but most of the previous models cannot sufficiently utilize document information, which limits the reasoning ability of the model. In this paper, we propose a novel Bio-DocuRE model called FILR, based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning. Specifically, FILR presents a multi-dimensional information fusion module MDIF to extract sufficient global document information. Then FILR proposes a multi-granularity reasoning module MGLR to obtain rich inference information through the reasoning of both entity-pairs and mention-pairs. We evaluate our FILR model on two widely used biomedical corpora CDR and GDA. Experimental results show that FILR achieves state-of-the-art performance.- Anthology ID:
- 2022.coling-1.183
- Volume:
- Proceedings of the 29th International Conference on Computational Linguistics
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Nicoletta Calzolari, Chu-Ren Huang, Hansaem Kim, James Pustejovsky, Leo Wanner, Key-Sun Choi, Pum-Mo Ryu, Hsin-Hsi Chen, Lucia Donatelli, Heng Ji, Sadao Kurohashi, Patrizia Paggio, Nianwen Xue, Seokhwan Kim, Younggyun Hahm, Zhong He, Tony Kyungil Lee, Enrico Santus, Francis Bond, Seung-Hoon Na
- Venue:
- COLING
- SIG:
- Publisher:
- International Committee on Computational Linguistics
- Note:
- Pages:
- 2098–2107
- Language:
- URL:
- https://aclanthology.org/2022.coling-1.183
- DOI:
- Cite (ACL):
- Lishuang Li, Ruiyuan Lian, Hongbin Lu, and Jingyao Tang. 2022. Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning. In Proceedings of the 29th International Conference on Computational Linguistics, pages 2098–2107, Gyeongju, Republic of Korea. International Committee on Computational Linguistics.
- Cite (Informal):
- Document-level Biomedical Relation Extraction Based on Multi-Dimensional Fusion Information and Multi-Granularity Logical Reasoning (Li et al., COLING 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.coling-1.183.pdf
- Code
- luguo-ry/filr