@inproceedings{sui-etal-2023-biofeg,
title = "{B}io{FEG}: Generate Latent Features for Biomedical Entity Linking",
author = "Sui, Xuhui and
Zhang, Ying and
Cai, Xiangrui and
Song, Kehui and
Zhou, Baohang and
Yuan, Xiaojie and
Zhang, Wensheng",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.710",
doi = "10.18653/v1/2023.emnlp-main.710",
pages = "11584--11593",
abstract = "Biomedical entity linking is an essential task in biomedical text processing, which aims to map entity mentions in biomedical text, such as clinical notes, to standard terms in a given knowledge base. However, this task is challenging due to the rarity of many biomedical entities in real-world scenarios, which often leads to a lack of annotated data for them. Limited by understanding these unseen entities, traditional biomedical entity linking models suffer from multiple types of linking errors. In this paper, we propose a novel latent feature generation framework BioFEG to address these challenges. Specifically, our BioFEG leverages domain knowledge to train a generative adversarial network, which generates latent semantic features of corresponding mentions for unseen entities. Utilizing these features, we fine-tune our entity encoder to capture fine-grained coherence information of unseen entities and better understand them. This allows models to make linking decisions more accurately, particularly for ambiguous mentions involving rare entities. Extensive experiments on the two benchmark datasets demonstrate the superiority of our proposed framework.",
}
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<abstract>Biomedical entity linking is an essential task in biomedical text processing, which aims to map entity mentions in biomedical text, such as clinical notes, to standard terms in a given knowledge base. However, this task is challenging due to the rarity of many biomedical entities in real-world scenarios, which often leads to a lack of annotated data for them. Limited by understanding these unseen entities, traditional biomedical entity linking models suffer from multiple types of linking errors. In this paper, we propose a novel latent feature generation framework BioFEG to address these challenges. Specifically, our BioFEG leverages domain knowledge to train a generative adversarial network, which generates latent semantic features of corresponding mentions for unseen entities. Utilizing these features, we fine-tune our entity encoder to capture fine-grained coherence information of unseen entities and better understand them. This allows models to make linking decisions more accurately, particularly for ambiguous mentions involving rare entities. Extensive experiments on the two benchmark datasets demonstrate the superiority of our proposed framework.</abstract>
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%0 Conference Proceedings
%T BioFEG: Generate Latent Features for Biomedical Entity Linking
%A Sui, Xuhui
%A Zhang, Ying
%A Cai, Xiangrui
%A Song, Kehui
%A Zhou, Baohang
%A Yuan, Xiaojie
%A Zhang, Wensheng
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F sui-etal-2023-biofeg
%X Biomedical entity linking is an essential task in biomedical text processing, which aims to map entity mentions in biomedical text, such as clinical notes, to standard terms in a given knowledge base. However, this task is challenging due to the rarity of many biomedical entities in real-world scenarios, which often leads to a lack of annotated data for them. Limited by understanding these unseen entities, traditional biomedical entity linking models suffer from multiple types of linking errors. In this paper, we propose a novel latent feature generation framework BioFEG to address these challenges. Specifically, our BioFEG leverages domain knowledge to train a generative adversarial network, which generates latent semantic features of corresponding mentions for unseen entities. Utilizing these features, we fine-tune our entity encoder to capture fine-grained coherence information of unseen entities and better understand them. This allows models to make linking decisions more accurately, particularly for ambiguous mentions involving rare entities. Extensive experiments on the two benchmark datasets demonstrate the superiority of our proposed framework.
%R 10.18653/v1/2023.emnlp-main.710
%U https://aclanthology.org/2023.emnlp-main.710
%U https://doi.org/10.18653/v1/2023.emnlp-main.710
%P 11584-11593
Markdown (Informal)
[BioFEG: Generate Latent Features for Biomedical Entity Linking](https://aclanthology.org/2023.emnlp-main.710) (Sui et al., EMNLP 2023)
ACL
- Xuhui Sui, Ying Zhang, Xiangrui Cai, Kehui Song, Baohang Zhou, Xiaojie Yuan, and Wensheng Zhang. 2023. BioFEG: Generate Latent Features for Biomedical Entity Linking. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 11584–11593, Singapore. Association for Computational Linguistics.