Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification

Pedro Ruas, Vitor Andrade, Francisco Couto


Abstract
The paper describes the participation of the Lasige-BioTM team at sub-tracks A and B of ProfNER, which was based on: i) a BiLSTM-CRF model that leverages contextual and classical word embeddings to recognize and classify the mentions, and ii) on a rule-based module to classify tweets. In the Evaluation phase, our model achieved a F1-score of 0.917 (0,031 more than the median) in sub-track A and a F1-score of 0.727 (0,034 less than the median) in sub-track B.
Anthology ID:
2021.smm4h-1.21
Volume:
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
Month:
June
Year:
2021
Address:
Mexico City, Mexico
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
108–111
Language:
URL:
https://aclanthology.org/2021.smm4h-1.21
DOI:
10.18653/v1/2021.smm4h-1.21
Bibkey:
Cite (ACL):
Pedro Ruas, Vitor Andrade, and Francisco Couto. 2021. Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 108–111, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification (Ruas et al., SMM4H 2021)
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PDF:
https://preview.aclanthology.org/ingestion-script-update/2021.smm4h-1.21.pdf