@inproceedings{mondal-etal-2019-medical,
title = "Medical Entity Linking using Triplet Network",
author = "Mondal, Ishani and
Purkayastha, Sukannya and
Sarkar, Sudeshna and
Goyal, Pawan and
Pillai, Jitesh and
Bhattacharyya, Amitava and
Gattu, Mahanandeeshwar",
editor = "Rumshisky, Anna and
Roberts, Kirk and
Bethard, Steven and
Naumann, Tristan",
booktitle = "Proceedings of the 2nd Clinical Natural Language Processing Workshop",
month = jun,
year = "2019",
address = "Minneapolis, Minnesota, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-1912",
doi = "10.18653/v1/W19-1912",
pages = "95--100",
abstract = "Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate Knowledge Base entries based on their similarity with disease mention. We make use of the Triplet Network for candidate ranking. While the existing methods have used carefully generated sieves and external resources for candidate generation, we introduce a robust and portable candidate generation scheme that does not make use of the hand-crafted rules. Experimental results on the standard benchmark NCBI disease dataset demonstrate that our system outperforms the prior methods by a significant margin.",
}
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%0 Conference Proceedings
%T Medical Entity Linking using Triplet Network
%A Mondal, Ishani
%A Purkayastha, Sukannya
%A Sarkar, Sudeshna
%A Goyal, Pawan
%A Pillai, Jitesh
%A Bhattacharyya, Amitava
%A Gattu, Mahanandeeshwar
%Y Rumshisky, Anna
%Y Roberts, Kirk
%Y Bethard, Steven
%Y Naumann, Tristan
%S Proceedings of the 2nd Clinical Natural Language Processing Workshop
%D 2019
%8 June
%I Association for Computational Linguistics
%C Minneapolis, Minnesota, USA
%F mondal-etal-2019-medical
%X Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given Knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate Knowledge Base entries based on their similarity with disease mention. We make use of the Triplet Network for candidate ranking. While the existing methods have used carefully generated sieves and external resources for candidate generation, we introduce a robust and portable candidate generation scheme that does not make use of the hand-crafted rules. Experimental results on the standard benchmark NCBI disease dataset demonstrate that our system outperforms the prior methods by a significant margin.
%R 10.18653/v1/W19-1912
%U https://aclanthology.org/W19-1912
%U https://doi.org/10.18653/v1/W19-1912
%P 95-100
Markdown (Informal)
[Medical Entity Linking using Triplet Network](https://aclanthology.org/W19-1912) (Mondal et al., ClinicalNLP 2019)
ACL
- Ishani Mondal, Sukannya Purkayastha, Sudeshna Sarkar, Pawan Goyal, Jitesh Pillai, Amitava Bhattacharyya, and Mahanandeeshwar Gattu. 2019. Medical Entity Linking using Triplet Network. In Proceedings of the 2nd Clinical Natural Language Processing Workshop, pages 95–100, Minneapolis, Minnesota, USA. Association for Computational Linguistics.