Medical Entity Linking using Triplet Network

Ishani Mondal, Sukannya Purkayastha, Sudeshna Sarkar, Pawan Goyal, Jitesh Pillai, Amitava Bhattacharyya, Mahanandeeshwar Gattu


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.
Anthology ID:
W19-1912
Volume:
Proceedings of the 2nd Clinical Natural Language Processing Workshop
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota, USA
Venue:
ClinicalNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
95–100
Language:
URL:
https://aclanthology.org/W19-1912
DOI:
10.18653/v1/W19-1912
Bibkey:
Cite (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.
Cite (Informal):
Medical Entity Linking using Triplet Network (Mondal et al., ClinicalNLP 2019)
Copy Citation:
PDF:
https://preview.aclanthology.org/author-url/W19-1912.pdf
Data
NCBI Disease