A Joint Training Approach to Tweet Classification and Adverse Effect Extraction and Normalization for SMM 4 H 2021 (2021)

Abstract: In this work we describe our submissions to the Social Media Mining for Health (SMM4H) 2021 Shared Task. We investigated the effectiveness of a joint training approach to Task 1, specifically classification, extraction and normalization of Adverse Drug Effect (ADE) mentions in English tweets. Our approach performed well on the normalization task, achieving an above average f1 score of 24%, but less so on classification and extraction, with f1 scores of 22% and 37% respectively. Our experiments also showed that a larger dataset with more negative results led to stronger results than a smaller more balanced dataset, even when both datasets have the same positive examples. Finally we also submitted a tuned BERT model for Task 6: Classification of Covid-19 tweets containing symptoms, which achieved an above average f1 score of 96%.

## introduction social media platforms such as twitter are regarded as potentially valuable tools for monitoring public health, including identifying ades to aid pharmacovigilance efforts. they do however pose a challenge due to the relative scarcity of relevant tweets in addition to a more fluid use of language, creating a further challenge of identifying and classifying specific instances of health-related issues. in this year's task as well as previous smm4h runs @xcite a distinction is made between classification, extraction, and normalization. this is atypical of ner systems, and many other ner datasets present their datasets, and are consequently solved in a joint approach. @xcite showed that simply tuning a base bert @xcite model could achieve strong results, even beating ensemble methods that rely on tranformers pretrained on more academic texts such as scibert @xcite , biobert @xcite or ensembles of them, while approaching the performance of bert models specifically pretrained on noisy health-related comments @xcite . ## conclusion in this work we explored the efficacy of jointly training a bert model to jointly learn to perform classification, extraction, and normalization of ade in tweets provided for task 1 in smmh 2021 shared task. while this approach did not produce classification and extraction above the median submission, it did achieve a normalization score that is. additionally our experiments show that the seemingly lopsided ratio of tweets with/without ades resulted in stronger performance than a more "balanced" dataset. finally, we showed that tuning a bert model produces very strong results on task 6, in classifying tweets related to covid-19.