Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs

Soumya Sharma, Bishal Santra, Abhik Jana, Santosh T.y.s.s, Niloy Ganguly, Pawan Goyal

Abstract
Recently, biomedical version of embeddings obtained from language models such as BioELMo have shown state-of-the-art results for the textual inference task in the medical domain. In this paper, we explore how to incorporate structured domain knowledge, available in the form of a knowledge graph (UMLS), for the Medical NLI task. Specifically, we experiment with fusing embeddings obtained from knowledge graph with the state-of-the-art approaches for NLI task (ESIM model). We also experiment with fusing the domain-specific sentiment information for the task. Experiments conducted on MedNLI dataset clearly show that this strategy improves the baseline BioELMo architecture for the Medical NLI task.
Anthology ID:
D19-1631
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Editors:
Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
6092–6097
Language:
URL:
https://aclanthology.org/D19-1631
DOI:
10.18653/v1/D19-1631
Bibkey:
Cite (ACL):
Soumya Sharma, Bishal Santra, Abhik Jana, Santosh T.y.s.s, Niloy Ganguly, and Pawan Goyal. 2019. Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 6092–6097, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs (Sharma et al., EMNLP-IJCNLP 2019)
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https://preview.aclanthology.org/nschneid-patch-5/D19-1631.pdf
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 D19-1631.Attachment.pdf
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 soummyaah/KGMedNLI
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