PAII - NLP at SMM 4 H 2021: Joint Extraction and Normalization of Adverse Drug Effect Mentions in Tweets (2021)

Abstract: This paper describes our system developed for the subtask 1c of the sixth Social Media Mining for Health Applications (SMM4H) shared task in 2021. The aim of the subtask is to recognize the adverse drug effect (ADE) mentions from tweets and normalize the identified mentions to their mapping MedDRA preferred term IDs. Our system is based on a neural transition-based joint model, which is to perform recognition and normalization simultaneously. Our final two submissions outperform the average F1 score by 1-2%.

## introduction with the popularity of social media such as twitter, people often publish messages online in regard to their health such as the information related to the adverse drug effects (ades). mining such type of information from social media is helpful for pharmacological post-marketing surveillance and monitoring. the aim of the sixth social media mining for health applications (smm4h) shared task in 2021 @xcite is to mining such invaluable health information from social media. we participate in the subtask 1c of smm4h 2021, which is to recognize the ade mentions from tweets and normalize the identified mentions to their mapping meddra 1 preferred term ids. ## task and data description we give the formal definition of the end-to-end task. briefly, given a tweet x published by a user, and a knowledge base (kb, i.e., meddra) which consists of a set of concepts, the goal of the task is to identify all the ade mentions @xmath0, m 2 , . mapping concept in kb for m i , then m @xmath1, where nil denotes that m i is unlinkable. we use meddra v21.1 as the kb, which consists 25,463 unique preferred term ids. ## the approach preprocessing. we preprocess all the tweets with the following steps @xcite : 1) tokenize the tweets with whitespace and punctuations; 2) lowercase the tokens; 3) replace the urls with "httpurl"; 4) replace the @user with "username"; 5) replace the escape characters with their original form (e.g., &amp; → &). we preprocess all the mentions and concepts in kb with the following steps @xcite : 1) replace the numerical words to their corresponding arabic numerals (e.g., one / first / i / @xmath2; 2) tokenize the mentions and concepts with whitespace and punctuations; 3) remove the punctuations; 4) lowercase the tokens. neural transition-based joint model. we cast the end-to-end task as a sequence labeling task and convert the whole task as an action sequence prediction task. we follow previous studies of applying neural transition-based model for named entity recognition (ner) @xcite with shift, out, reduce, seg-ment actions for the recognition purpose and further extend the model by adding linking actions for the normalization purpose. input representation we represent each token x i in a tweet x by concatenating its character-level word representation, non-contextual word representation, and contextual word representation: search and training for efficient decoding, a widely-used greedy search algorithm @xcite ) is adopted to minimize the negative log-likelihood of the local action classifier, i.e., to minimize the cross-entropy loss between the output distribution with the gold-standard distribution: ## results and conclusions we submit the following two results with two different strategies: we report the precision, recall and f1 for each ade extracted where the spans overlap either entirely or partially and each span is normalized to the correct meddra preferred term id. in the future, we will further tune the model and explore other popular contextual word representations learned from bert @xcite .