Author: Sancheti, Abhilasha; Chawla, Kushal; Verma, Gaurav
Title: LynyrdSkynyrd at WNUT-2020 Task 2: Semi-Supervised Learning for Identification of Informative COVID-19 English Tweets Cord-id: gflg86fh Document date: 2020_9_8
ID: gflg86fh
Snippet: 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 m
Document: 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.
Search related documents:
Co phrase search for related documents- ablation study and long range: 1
- absence presence and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
- absence presence and logistic regression ensemble: 1
- absence presence and long range: 1
- logistic regression and long range: 1, 2, 3, 4, 5, 6, 7
Co phrase search for related documents, hyperlinks ordered by date