@inproceedings{phan-etal-2019-robust,
title = "Robust Representation Learning of Biomedical Names",
author = "Phan, Minh C. and
Sun, Aixin and
Tay, Yi",
editor = "Korhonen, Anna and
Traum, David and
M{\`a}rquez, Llu{\'\i}s",
booktitle = "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P19-1317",
doi = "10.18653/v1/P19-1317",
pages = "3275--3285",
abstract = "Biomedical concepts are often mentioned in medical documents under different name variations (synonyms). This mismatch between surface forms is problematic, resulting in difficulties pertaining to learning effective representations. Consequently, this has tremendous implications such as rendering downstream applications inefficacious and/or potentially unreliable. This paper proposes a new framework for learning robust representations of biomedical names and terms. The idea behind our approach is to consider and encode contextual meaning, conceptual meaning, and the similarity between synonyms during the representation learning process. Via extensive experiments, we show that our proposed method outperforms other baselines on a battery of retrieval, similarity and relatedness benchmarks. Moreover, our proposed method is also able to compute meaningful representations for unseen names, resulting in high practical utility in real-world applications.",
}
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%0 Conference Proceedings
%T Robust Representation Learning of Biomedical Names
%A Phan, Minh C.
%A Sun, Aixin
%A Tay, Yi
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F phan-etal-2019-robust
%X Biomedical concepts are often mentioned in medical documents under different name variations (synonyms). This mismatch between surface forms is problematic, resulting in difficulties pertaining to learning effective representations. Consequently, this has tremendous implications such as rendering downstream applications inefficacious and/or potentially unreliable. This paper proposes a new framework for learning robust representations of biomedical names and terms. The idea behind our approach is to consider and encode contextual meaning, conceptual meaning, and the similarity between synonyms during the representation learning process. Via extensive experiments, we show that our proposed method outperforms other baselines on a battery of retrieval, similarity and relatedness benchmarks. Moreover, our proposed method is also able to compute meaningful representations for unseen names, resulting in high practical utility in real-world applications.
%R 10.18653/v1/P19-1317
%U https://aclanthology.org/P19-1317
%U https://doi.org/10.18653/v1/P19-1317
%P 3275-3285
Markdown (Informal)
[Robust Representation Learning of Biomedical Names](https://aclanthology.org/P19-1317) (Phan et al., ACL 2019)
ACL
- Minh C. Phan, Aixin Sun, and Yi Tay. 2019. Robust Representation Learning of Biomedical Names. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 3275–3285, Florence, Italy. Association for Computational Linguistics.