NLP - CIC - WFU at S ocial D is NER : Disease Mention Extraction in S panish Tweets Using Transfer Learning and Search by Propagation (2022)

Abstract: Named entity recognition (e.g., disease mention extraction) is one of the most relevant tasks for data mining in the medical field. Although it is a well-known challenge, the bulk of the efforts to tackle this task have been made using clinical texts commonly written in English. In this work, we present our contribution to the SocialDisNER competition, which consists of a transfer learning approach to extracting disease mentions in a corpus from Twitter written in Spanish. We fine-tuned a model based on mBERT and applied post-processing using regular expressions to propagate the entities identified by the model and enhance disease mention extraction. Our system achieved a competitive strict F1 of 0.851 on the testing data set.

## motivation although there are several works for disease mention extraction, the bulk of them has been carried out for clinical texts written in english @xcite @xcite @xcite @xcite @xcite . in this work, we present our contribution to the socialdisner competition @xcite at the smm4h workshop, task 10 @xcite . our system is focused on disease mention extraction from twitter messages in spanish. the nature of the texts written in this social network presents new challenges to the disease extraction task because misspellings are frequent @xcite . additionally, many disease mentions can be subsumed in hashtags, urls, or user names @xcite , which, together with the above, makes it more difficult to identify entities than in common clinical texts as shown in @xcite ; garcía-pablos et al. (2020); @xcite . ## system description in this work, we present a transfer learning approach using the model proposed by @xcite , which is a version of multilingual bert @xcite fine-tuned for disease mention extraction from clinical texts and we apply postprocessing rules to extract diseases mentioned in a corpus of tweets in spanish. our system tackles the problem in three steps, namely, pre-processing, transfer learning, and post-processing. below we describe each of them. ## results and error analysis the results achieved by our model on the validation dataset can be seen in ## conclusions in this work, we presented a system based on mbert following a fine-tuning approach plus simple post-processing and search by propagation to extract disease mentions from tweets in spanish. we achieved competitive results with a strict f1 of 0.851, precision of 0.842, and recall of 0.860 on the test dataset of the socialdisner competition.