@inproceedings{ruas-etal-2021-lasige,
title = "Lasige-{B}io{TM} at {P}rof{NER}: {B}i{LSTM}-{CRF} and contextual {S}panish embeddings for Named Entity Recognition and Tweet Binary Classification",
author = "Ruas, Pedro and
Andrade, Vitor and
Couto, Francisco",
editor = "Magge, Arjun and
Klein, Ari and
Miranda-Escalada, Antonio and
Al-garadi, Mohammed Ali and
Alimova, Ilseyar and
Miftahutdinov, Zulfat and
Farre-Maduell, Eulalia and
Lopez, Salvador Lima and
Flores, Ivan and
O'Connor, Karen and
Weissenbacher, Davy and
Tutubalina, Elena and
Sarker, Abeed and
Banda, Juan M and
Krallinger, Martin and
Gonzalez-Hernandez, Graciela",
booktitle = "Proceedings of the Sixth Social Media Mining for Health ({\#}SMM4H) Workshop and Shared Task",
month = jun,
year = "2021",
address = "Mexico City, Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.smm4h-1.21",
doi = "10.18653/v1/2021.smm4h-1.21",
pages = "108--111",
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.",
}
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%0 Conference Proceedings
%T Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification
%A Ruas, Pedro
%A Andrade, Vitor
%A Couto, Francisco
%Y Magge, Arjun
%Y Klein, Ari
%Y Miranda-Escalada, Antonio
%Y Al-garadi, Mohammed Ali
%Y Alimova, Ilseyar
%Y Miftahutdinov, Zulfat
%Y Farre-Maduell, Eulalia
%Y Lopez, Salvador Lima
%Y Flores, Ivan
%Y O’Connor, Karen
%Y Weissenbacher, Davy
%Y Tutubalina, Elena
%Y Sarker, Abeed
%Y Banda, Juan M.
%Y Krallinger, Martin
%Y Gonzalez-Hernandez, Graciela
%S Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
%D 2021
%8 June
%I Association for Computational Linguistics
%C Mexico City, Mexico
%F ruas-etal-2021-lasige
%X 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.
%R 10.18653/v1/2021.smm4h-1.21
%U https://aclanthology.org/2021.smm4h-1.21
%U https://doi.org/10.18653/v1/2021.smm4h-1.21
%P 108-111
Markdown (Informal)
[Lasige-BioTM at ProfNER: BiLSTM-CRF and contextual Spanish embeddings for Named Entity Recognition and Tweet Binary Classification](https://aclanthology.org/2021.smm4h-1.21) (Ruas et al., SMM4H 2021)
ACL