Team Yseop at # SMM 4 H 2024: Multilingual Pharmacovigilance Named Entity Recognition and Relation Extraction (2024)

Abstract: This paper describes three RoBERTa based systems. The first one recognizes adverse drug events (ADEs) in English tweets and links themwith MedDRA concepts. It scored F1-norm of 40 for the Task 1. The next one extracts pharmacovigilance related named entities inFrench and scored a F1 of 0.4132 for the Task 2a. The third system extracts pharmacovigilance related named entities and their relationsin Japanese. It obtained a F1 of 0.5827 for the Task 2a and 0.0301 for the Task 2b. The French and Japanese systems are the best performing system for the Task 2

## introduction as of 2008, the european commission proposed certain measures foot_1 to protect the public from the harm caused by adverse drug reaction (adr). nonetheless, @xcite observed a global increase in adverse drug event (ade) related deaths. thus, pharmacovigilance, i.e. activities involving detection, comprehension, and prevention of adverse effects due to medication is an important subject. with the arrival of internet, the general public started using it to seek and share health related information 3 @xcite . with the help of natural language processing systems, this publicly available data can be analysed to extract information related to side effects. as a result, it can play a key role in strengthening pharmacovigilance reporting systems. note: even though, the "ich e2a clinical safety data management: definitions and standards for expedited reporting" foot_3 distinguishes between adr and ade, in this paper we will use the terms interchangeably to refer to the unintended consequences of taking a prescribed medication. smm4h-2024: the 9th social media mining for health research and applications workshop and shared tasks -large language models (llms) and generalizability for social media nlp @xcite proposed 7 shared tasks. we participated in two of them: in this task, we have to identify ades in a short text, called "tweets", written in english. if ades are present, then they have to be mapped to preferred terms id (ptid) from the medical dictionary for regulatory activities (meddra) 5 . due to an error on our part, we did this task with the smm4h -2023 task 5 dataset, shared on the task's google groups on 06/02/2024. ## task 2 -french and japanese the goals of this task were named entity recognition (ner) and relation extraction (re). the corpus consisted of, mainly, german and japanese text taken from online forums and twitter/x. it also had four french documents translated from german texts. ## conclusion in task 1, despite training on the wrong dataset we managed to be in the top 50 percentile. the difficult part was normalization of the ade using meddra dictionary, as a result f1-norm was lower than f1-ner. for the task 2, using a model adapted to the clinical domain helped to get the best results. the task 2b (relation extraction) was challenging, given that the winning team obtained an overall f1 score of 0.0189. for future, we would explore approaches such as gliner @xcite and xmc @xcite to improve ner in task 1 and task 2a. zexuan zhong and danqi chen. 2021. a frustratingly easy approach for entity and relation extraction. in