This paper describes the models developed by the AILAB-Udine team for the SMM4H’22 Shared Task.
Keyphrase Generation is the task of predicting Keyphrases (KPs), short phrases that summarize the semantic meaning of a given document.
Medical term normalization consists in mapping a piece of text to a large number of output classes.
This paper describes the models developed by the AILAB-Udine team for the SMM4H 22 Shared Task.
In the last decade, an increasing number of users have started reporting Adverse Drug Events (ADE) on social media platforms, blogs, and health forums.
Adverse Drug Event (ADE) extraction models can rapidly examine large collections of social media texts, detecting mentions of drug-related adverse reactions and trigger medical investigations.
Our results show that: workers are able to detect and objectively categorize online (mis)information related to COVID-19; both crowdsourced and expert judgments can be transformed and aggregated to improve quality; worker background and other signals (e. g., source of information, behavior) impact the quality of the data.
In recent years, Internet users are reporting Adverse Drug Events (ADE) on social media, blogs and health forums.
Pretrained transformer-based models, such as BERT and its variants, have become a common choice to obtain state-of-the-art performances in NLP tasks.
Misinformation is an ever increasing problem that is difficult to solve for the research community and has a negative impact on the society at large.
We propose, instead, a model-agnostic framework that consists of two modules: (1) a span extractor, which identifies the crucial information connecting claim and evidence; and (2) a classifier that combines claim, evidence, and the extracted spans to predict the veracity of the claim.