Pith. sign in

REVIEW

COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 2005.07503 v1 pith:TPA63KA4 submitted 2020-05-15 cs.CL cs.LGcs.SI

classification cs.CLcs.LGcs.SI
keywords modelcovid-19ct-berttwitterclassificationcontentcovid-twitter-bertdomain
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

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.

Discussion (0). Continue with ORCID to comment.

Pith tools