@inproceedings{yan-etal-2021-biomedical,
title = "Biomedical Concept Normalization by Leveraging Hypernyms",
author = "Yan, Cheng and
Zhang, Yuanzhe and
Liu, Kang and
Zhao, Jun and
Shi, Yafei and
Liu, Shengping",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.284",
doi = "10.18653/v1/2021.emnlp-main.284",
pages = "3512--3517",
abstract = "Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module. Owing to numerous surface variants of biomedical concepts, BCN still remains challenging and unsolved. In this paper, we exploit biomedical concept hypernyms to facilitate BCN. We propose Biomedical Concept Normalizer with Hypernyms (BCNH), a novel framework that adopts list-wise training to make use of both hypernyms and synonyms, and also employs norm constraint on the representation of hypernym-hyponym entity pairs. The experimental results show that BCNH outperforms the previous state-of-the-art model on the NCBI dataset.",
}
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<abstract>Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module. Owing to numerous surface variants of biomedical concepts, BCN still remains challenging and unsolved. In this paper, we exploit biomedical concept hypernyms to facilitate BCN. We propose Biomedical Concept Normalizer with Hypernyms (BCNH), a novel framework that adopts list-wise training to make use of both hypernyms and synonyms, and also employs norm constraint on the representation of hypernym-hyponym entity pairs. The experimental results show that BCNH outperforms the previous state-of-the-art model on the NCBI dataset.</abstract>
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%0 Conference Proceedings
%T Biomedical Concept Normalization by Leveraging Hypernyms
%A Yan, Cheng
%A Zhang, Yuanzhe
%A Liu, Kang
%A Zhao, Jun
%A Shi, Yafei
%A Liu, Shengping
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F yan-etal-2021-biomedical
%X Biomedical Concept Normalization (BCN) is widely used in biomedical text processing as a fundamental module. Owing to numerous surface variants of biomedical concepts, BCN still remains challenging and unsolved. In this paper, we exploit biomedical concept hypernyms to facilitate BCN. We propose Biomedical Concept Normalizer with Hypernyms (BCNH), a novel framework that adopts list-wise training to make use of both hypernyms and synonyms, and also employs norm constraint on the representation of hypernym-hyponym entity pairs. The experimental results show that BCNH outperforms the previous state-of-the-art model on the NCBI dataset.
%R 10.18653/v1/2021.emnlp-main.284
%U https://aclanthology.org/2021.emnlp-main.284
%U https://doi.org/10.18653/v1/2021.emnlp-main.284
%P 3512-3517
Markdown (Informal)
[Biomedical Concept Normalization by Leveraging Hypernyms](https://aclanthology.org/2021.emnlp-main.284) (Yan et al., EMNLP 2021)
ACL
- Cheng Yan, Yuanzhe Zhang, Kang Liu, Jun Zhao, Yafei Shi, and Shengping Liu. 2021. Biomedical Concept Normalization by Leveraging Hypernyms. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3512–3517, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.