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Covid-twitter-bert: A Natural Language Processing Model To Analyse COVID-19 Content On Twitter

Martin Müller, Marcel Salathé, Per E Kummervold . Frontiers in Artificial Intelligence 2023 – 159 citations

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Compositional Generalization Content Enrichment Datasets Image Text Integration Interactive Environments Interdisciplinary Approaches Model Architecture Multimodal Semantic Representation Neural Machine Translation Productivity Enhancement Question Answering

In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19. Our model shows a 10-30% marginal improvement compared to its base model, BERT-Large, on five different classification datasets. The largest improvements are on the target domain. Pretrained transformer models, such as CT-BERT, are trained on a specific target domain and can be used for a wide variety of natural language processing tasks, including classification, question-answering and chatbots. CT-BERT is optimised to be used on COVID-19 content, in particular social media posts from Twitter.

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