IxaMed at PharmacoNER Challenge 2019

0Citations
Citations of this article
62Readers
Mendeley users who have this article in their library.

Abstract

The aim of this paper is to present our approach (IxaMed) on the PharmacoNER 2019 task. The task consists of identifying chemical, drug, and gene/protein mentions from clinical case studies written in Spanish. The evaluation of the task is divided in two scenarios: one corresponding to the detection of named entities and one corresponding to the indexation of named entities that have been previously identified. In order to identify named entities we have made use of a Bi-LSTM with a CRF on top in combination with different types of word embeddings. We have achieved our best result (86.81 F-Score) combining pretrained word embeddings ofWikipedia and Electronic Health Records (50M words) with contextual string embeddings ofWikipedia and Electronic Health Records. On the other hand, for the indexation of the named entities we have used the Levenshtein distance obtaining a 85.34 F-Score as our best result. c 2019 Association for Computational Linguistics.

Cite

CITATION STYLE

APA

Lahuerta, X., Goenaga, I., Gojenola, K., Atutxa, A., & Oronoz, M. (2019). IxaMed at PharmacoNER Challenge 2019. In BioNLP-OST@EMNLP-IJNCLP 2019 - Proceedings of the 5th Workshop on BioNLP Open Shared Tasks (pp. 21–25). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d19-5704

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free