dezzai@ SMM 4 H ’22: Tasks 5 & 10 - Hybrid models everywhere (2022)

Abstract: This paper presents our approaches to SMM4H’22 task 5 - Classification of tweets of self-reported COVID-19 symptoms in Spanish, and task 10 - Detection of disease mentions in tweets – SocialDisNER (in Spanish). We have presented hybrid systems that combine Deep Learning techniques with linguistic rules and medical ontologies, which have allowed us to achieve outstanding results in both tasks.

## introduction the social media mining for health applications (smm4h) @xcite workshop aims to promote automatic methods for mining social media data for health informatics. in order to support spanish natural language processing (nlp), we have focused on both spanish tasks. the smm4h 2022 task 5 focuses on classification of tweets containing self-reports of covid-19 symptoms in spanish. it consists of a triple classification task in which participants have to distinguish personal symptoms from symptoms reported by others and references to news articles or other sources, giving rise to three labels: nonpersonal reports, news and literature mentions, and self-reports. in the case of smm4h task 10 -detection of disease mentions in tweets -socialdisner (in spanish) @xcite , the task is focused on the recognition of disease mentions in tweets written in spanish, with the aim of using social media to better understand societal perception of diseases. therefore, we have tackled this task as a ner offset detection and token classification problem. our contributions to these tasks are the following: our pipeline is based on a linguistic preprocessing that allows us to split the tweets in two subgroups, with each of which we use a different roberta @xcite classifier. in first place we apply a filter where two subsets of the training set are created. for each tweet we analyze the number of "first person flags", that is, the number of first person verbs and pronouns, and the number of "other person flags", that is, the number of verbs which are not in first person. then we use a threshold to compare this values: if at least a third of the total flags correspond "first person flags" the tweet is added to subset 1, and else to subset 2. this way only the tweets which the system filter as subset 1 can be labeled as selfreport. in second place, we trained 2 xlm roberta base classifiers: one ternary classifier trained on all 3 labels from training set, and one binary classifier with only the "news and literature mentions" and the "non-personal report" labeled tweets from the training set. subsequently, during inference, we apply the previously explained filter on the test dataset. in the end the 3-label classifier is used for subset 1 and the binary classifier for subset 2. this ## conclusions in this paper we have reviewed our approaches to smm4h'22 tasks 5 and 10. for both tasks we have developed hybrid systems combining deep learning techniques with rule-based modules. we have analysed our results as well as both datasets, and presented some insights of each one. we also reviewed the limitations of our models and highlighted their weaknesses and strengths. as seen in we consider this two tasks specially relevant due to the need to promote nlp in spanish, and even more so when applied to the clinical domain using real data.