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
- 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)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2021.smm4h-1.21.pdf