Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention

Qin Dai, Naoya Inoue, Paul Reisert, Ryo Takahashi, Kentaro Inui


Abstract
The increased demand for structured scientific knowledge has attracted considerable attention in extracting scientific relation from the ever growing scientific publications. Distant supervision is widely applied approach to automatically generate large amounts of labelled data with low manual annotation cost. However, distant supervision inevitably accompanies the wrong labelling problem, which will negatively affect the performance of Relation Extraction (RE). To address this issue, (Han et al., 2018) proposes a novel framework for jointly training both RE model and Knowledge Graph Completion (KGC) model to extract structured knowledge from non-scientific dataset. In this work, we firstly investigate the feasibility of this framework on scientific dataset, specifically on biomedical dataset. Secondly, to achieve better performance on the biomedical dataset, we extend the framework with other competitive KGC models. Moreover, we proposed a new end-to-end KGC model to extend the framework. Experimental results not only show the feasibility of the framework on the biomedical dataset, but also indicate the effectiveness of our extensions, because our extended model achieves significant and consistent improvements on distant supervised RE as compared with baselines.
Anthology ID:
W19-2601
Volume:
Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–10
Language:
URL:
https://aclanthology.org/W19-2601
DOI:
10.18653/v1/W19-2601
Bibkey:
Cite (ACL):
Qin Dai, Naoya Inoue, Paul Reisert, Ryo Takahashi, and Kentaro Inui. 2019. Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention. In Proceedings of the Workshop on Extracting Structured Knowledge from Scientific Publications, pages 1–10, Minneapolis, Minnesota. Association for Computational Linguistics.
Cite (Informal):
Distantly Supervised Biomedical Knowledge Acquisition via Knowledge Graph Based Attention (Dai et al., NAACL 2019)
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PDF:
https://aclanthology.org/W19-2601.pdf