@inproceedings{sharma-etal-2019-incorporating,
title = "Incorporating Domain Knowledge into Medical {NLI} using Knowledge Graphs",
author = "Sharma, Soumya and
Santra, Bishal and
Jana, Abhik and
T.y.s.s, Santosh and
Ganguly, Niloy and
Goyal, Pawan",
editor = "Inui, Kentaro and
Jiang, Jing and
Ng, Vincent and
Wan, Xiaojun",
booktitle = "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 = nov,
year = "2019",
address = "Hong Kong, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D19-1631",
doi = "10.18653/v1/D19-1631",
pages = "6092--6097",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs
%A Sharma, Soumya
%A Santra, Bishal
%A Jana, Abhik
%A T.y.s.s, Santosh
%A Ganguly, Niloy
%A Goyal, Pawan
%Y Inui, Kentaro
%Y Jiang, Jing
%Y Ng, Vincent
%Y Wan, Xiaojun
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F sharma-etal-2019-incorporating
%X 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.
%R 10.18653/v1/D19-1631
%U https://aclanthology.org/D19-1631
%U https://doi.org/10.18653/v1/D19-1631
%P 6092-6097
Markdown (Informal)
[Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs](https://aclanthology.org/D19-1631) (Sharma et al., EMNLP-IJCNLP 2019)
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.