IxaMed at PharmacoNER Challenge 2019

Xabier Lahuerta, Iakes Goenaga, Koldo Gojenola, Aitziber Atutxa Salazar, Maite Oronoz


Abstract
The aim of this paper is to present our approach (IxaMed) in 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 of Wikipedia and Electronic Health Records (50M words) with contextual string embeddings of Wikipedia 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.
Anthology ID:
D19-5704
Volume:
Proceedings of The 5th Workshop on BioNLP Open Shared Tasks
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
BioNLP | EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
21–25
Language:
URL:
https://www.aclweb.org/anthology/D19-5704
DOI:
10.18653/v1/D19-5704
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PDF:
http://aclanthology.lst.uni-saarland.de/D19-5704.pdf