B io I nfo@ UAVR @ SMM 4 H ’22: Classification and Extraction of Adverse Event mentions in Tweets using Transformer Models (2022)

Abstract: This paper describes BioInfo@UAVR team’s approach for adressing subtasks 1a and 1b of the Social Media Mining for Health Applications 2022 shared task. These sub-tasks deal with the classification of tweets that contain an Adverse Drug Event mentions and the detection of spans that correspond to those mentions. Our approach relies on transformer-based models, data augmentation, and an external dataset.

## introduction the social media mining for health applications shared task @xcite addresses challenges relating to the use of social media data for health research. our team participated in subtasks 1a and 1b. subtask 1a focuses on the classification of tweets regarding the presence of adverse drug events (ades) and subtask 1b focuses on the detection of ade spans in tweets. in this submission, we performed some experiments that explore the use of transformer-based models to solve the tackled tasks. ## datasets in the experiments executed for these subtasks, we used the datasets provided by the organizers @xcite , which were annotated for each of the subtasks. the training set had 17385 labeled tweets (1239 labeled ade), the validation set had 915 labeled tweets (65 labeled ade) and the test set had 10984 unlabeled tweets. furthermore, an additional dataset was used in the training phase for subtask 1b, along with different balancing techniques. ## results and discussion the submission for subtask 1a was obtained through the training of a bertweet-large classification model with the augmented training and validation data. for subtask 1b we used a bertweetlarge ner model trained on the training data with positive examples from the webradr dataset. the submission for subtask 1b was obtained by using the ner system on the predictions submitted for subtask 1a. the results from the final submission are in tables 4 and 5 . in subtask 1a, we obtained results above the average in every metric, however in subtask 1b the only metrics above the average values were the precision metrics. ## conclusion we proposed a text classification and ner pipeline which address the challenges posed by subtask 1a and 1b. our system was able to achieve a f1 score of 0.698 on subtask 1a and a strict f1 score of 0.331 on subtask 1b.