Work

#GCDH at WNUT-2020 Task 2: BERT-Based Models for the Detection of Informativeness in English COVID-19 Related Tweets

Year: 2020

Type: conference-paper

Abstract: In this system paper, we present a transformerbased approach to the detection of informativeness in English tweets on the topic of the current COVID-19 pandemic.Our models distinguish informative tweets, i.e. tweets containing statistics on recovery, suspected and confirmed cases and COVID-19 related deaths, from uninformative tweets.We present two transformer-based approaches as well as a Naive Bayes classifier and a support vector machine as baseline systems.The transformer models outperform the baselines by more than 0.1 in F1-score, with F1-scores of 0.9091 and 0.9036.Our models were submitted to the shared task Identification of informative COVID-19 English tweets (WNUT-2020 Task 2).

Source: Workshop on Noisy User-generated Text (W-NUT)

DOI: 10.18653/v1/2020.wnut-1.68

Authors: Hanna Varachkina, Stefan Ziehe, Tillmann Dönicke, Franziska Pannach

Institution: University of Göttingen

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W3101547274