LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets

TL;DR

This paper presents an ensemble semi-supervised learning system combining traditional classifiers and pre-trained language models to identify informative COVID-19 tweets, achieving high F1-scores.

Contribution

It introduces a novel ensemble approach that integrates pseudo-labeling with advanced language models for tweet classification during the COVID-19 pandemic.

Findings

Achieved an F1-score of 0.9179 on validation set.

Achieved an F1-score of 0.8805 on test set.

Demonstrated effectiveness of semi-supervised ensemble methods.

Abstract

We describe our system for WNUT-2020 shared task on the identification of informative COVID-19 English tweets. Our system is an ensemble of various machine learning methods, leveraging both traditional feature-based classifiers as well as recent advances in pre-trained language models that help in capturing the syntactic, semantic, and contextual features from the tweets. We further employ pseudo-labelling to incorporate the unlabelled Twitter data released on the pandemic. Our best performing model achieves an F1-score of 0.9179 on the provided validation set and 0.8805 on the blind test-set.