READ - B io M ed@ S ocial D is NER : Adaptation of an Annotation System to S panish Tweets (2022)

Abstract: We describe the work of the READ-BioMed team for the preparation of a submission to the SocialDisNER Disease Named Entity Recognition (NER) Task (Task 10) in 2022. We had developed a system for named entity recognition for identifying biomedical concepts in English MEDLINE citations and Spanish clinical text for the LivingNER 2022 challenge. Minimal adaptation of our system was required to perform named entity recognition in the Spanish tweets in the SocialDisNER task, given the availability of Spanish pre-trained language models and the SocialDisNER training data. Minor additions included treatment of emojis and entities in hashtags and Twitter account names.

## motivation in this paper, we describe the read-biomed (reading, extraction, and annotation of documents in biomedicine) approach to the 2022 so-cialdisner task 10 @xcite . the documents in this task are spanish-language tweets and the task involves the annotation of disease entities in tweet text. the read-biomed team has extensive experience in the processing of twitter to identify medical entities @xcite , the analysis of large sets of tweets for trend analysis @xcite @xcite , pharmacovigilance using social media @xcite , and for syndromic surveillance @xcite . our approach was to adapt a system previously developed for annotation of medline citations in the english language and clinical text in spanish. our system relies on a pre-trained transformer based language model, which was fine-tuned for biomedical concept recognition for the litcoin challenge earlier this year foot_0 , where we ranked in the top 5 submissions foot_1 . more recently, we adapted this system for processing spanish clinical texts (jimeno yepes and verspoor, 2022) in the livingner challenge @xcite . ## methods in this section, we describe the methods that we have used in the challenge. we describe the data and the pre-processing step, the training and annotation of the data and the post-processing steps that we have followed to prepare our submissions using the validation and testing sets. ## results we generated only one submission for the testing set using the procedure presented above. results of our submission (read-biomed) using strict entity matching are presented in