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BERT based Transformers lead the way in Extraction of Health Information from Social Media

Sidharth R, Abhiraj Tiwari, Parthivi Choubey, Saisha Kashyap, Sahil Khose, Kumud Lakara, Nishesh Singh, Ujjwal Verma

NAACL Paper

Submission to SMM4H

This repo contains the codes for Task 1a, 1b, Task 6 of the Social Media Mining for Health Applications Workshop. The proceedings are available here

Abstract

This paper describes our submissions for the Social Media Mining for Health (SMM4H)2021 shared tasks. We participated in 2 tasks:(1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms(Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61%. For subtask-1(b), our system obtained an F1-score of 50% with improvements up to +8% F1 over the score averaged across all submissions. The BERTweet model achieved an F1 score of 94% on SMM4H 2021 Task-6.

Results

Task 1a

We ranked 1st on the test set (F1).

Precision Recall F1
RoBERTa (our) 0.515 0.752 0.61
Median 0.505 0.409 0.44

Task 1b

We ranked 2nd on the test set (F1).

Precision Recall F1
en_core_web_trf (our) 0.493 0.505 0.50
Median 0.493 0.458 0.42

Task 6

We ranked 2nd on the test set (F1).

Precision Recall F1
BERTweet (our) 0.94 0.94 0.94
Median 0.93 0.93 0.93

📚 Citation

If you find our paper useful in your research, please consider citing:

@inproceedings{ramesh-etal-2021-bert,
    title = "{BERT} based Transformers lead the way in Extraction of Health Information from Social Media",
    author = "Ramesh, Sidharth  and
      Tiwari, Abhiraj  and
      Choubey, Parthivi  and
      Kashyap, Saisha  and
      Khose, Sahil  and
      Lakara, Kumud  and
      Singh, Nishesh  and
      Verma, Ujjwal",
    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://www.aclweb.org/anthology/2021.smm4h-1.5",
    pages = "33--38",
    abstract = "This paper describes our submissions for the Social Media Mining for Health (SMM4H) 2021 shared tasks. We participated in 2 tasks: (1) Classification, extraction and normalization of adverse drug effect (ADE) mentions in English tweets (Task-1) and (2) Classification of COVID-19 tweets containing symptoms (Task-6). Our approach for the first task uses the language representation model RoBERTa with a binary classification head. For the second task, we use BERTweet, based on RoBERTa. Fine-tuning is performed on the pre-trained models for both tasks. The models are placed on top of a custom domain-specific pre-processing pipeline. Our system ranked first among all the submissions for subtask-1(a) with an F1-score of 61{\%}. For subtask-1(b), our system obtained an F1-score of 50{\%} with improvements up to +8{\%} F1 over the median score across all submissions. The BERTweet model achieved an F1 score of 94{\%} on SMM4H 2021 Task-6.",
}