@inproceedings{sakhovskiy-etal-2021-kfu,
title = "{KFU} {NLP} Team at {SMM}4{H} 2021 Tasks: Cross-lingual and Cross-modal {BERT}-based Models for Adverse Drug Effects",
author = "Sakhovskiy, Andrey and
Miftahutdinov, Zulfat and
Tutubalina, Elena",
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-1.6",
doi = "10.18653/v1/2021.smm4h-1.6",
pages = "39--43",
abstract = "This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task. We participated in two tasks on classification of tweets that mention an adverse drug effect (ADE) (Tasks 1a {\&} 2) and two tasks on extraction of ADE concepts (Tasks 1b {\&} 1c). For classification, we investigate the impact of joint use of BERTbased language models and drug embeddings obtained by chemical structure BERT-based encoder. The BERT-based multimodal models ranked first and second on classification of Russian (Task 2) and English tweets (Task 1a) with the F1 scores of 57{\%} and 61{\%}, respectively. For Task 1b and 1c, we utilized the previous year{'}s best solution based on the EnDR-BERT model with additional corpora. Our model achieved the best results in Task 1c, obtaining an F1 of 29{\%}.",
}
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<abstract>This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task. We participated in two tasks on classification of tweets that mention an adverse drug effect (ADE) (Tasks 1a & 2) and two tasks on extraction of ADE concepts (Tasks 1b & 1c). For classification, we investigate the impact of joint use of BERTbased language models and drug embeddings obtained by chemical structure BERT-based encoder. The BERT-based multimodal models ranked first and second on classification of Russian (Task 2) and English tweets (Task 1a) with the F1 scores of 57% and 61%, respectively. For Task 1b and 1c, we utilized the previous year’s best solution based on the EnDR-BERT model with additional corpora. Our model achieved the best results in Task 1c, obtaining an F1 of 29%.</abstract>
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%0 Conference Proceedings
%T KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects
%A Sakhovskiy, Andrey
%A Miftahutdinov, Zulfat
%A Tutubalina, Elena
%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 sakhovskiy-etal-2021-kfu
%X This paper describes neural models developed for the Social Media Mining for Health (SMM4H) 2021 Shared Task. We participated in two tasks on classification of tweets that mention an adverse drug effect (ADE) (Tasks 1a & 2) and two tasks on extraction of ADE concepts (Tasks 1b & 1c). For classification, we investigate the impact of joint use of BERTbased language models and drug embeddings obtained by chemical structure BERT-based encoder. The BERT-based multimodal models ranked first and second on classification of Russian (Task 2) and English tweets (Task 1a) with the F1 scores of 57% and 61%, respectively. For Task 1b and 1c, we utilized the previous year’s best solution based on the EnDR-BERT model with additional corpora. Our model achieved the best results in Task 1c, obtaining an F1 of 29%.
%R 10.18653/v1/2021.smm4h-1.6
%U https://aclanthology.org/2021.smm4h-1.6
%U https://doi.org/10.18653/v1/2021.smm4h-1.6
%P 39-43
Markdown (Informal)
[KFU NLP Team at SMM4H 2021 Tasks: Cross-lingual and Cross-modal BERT-based Models for Adverse Drug Effects](https://aclanthology.org/2021.smm4h-1.6) (Sakhovskiy et al., SMM4H 2021)
ACL