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

09/08/2020
by   Abhilasha Sancheti, et al.
0

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.

READ FULL TEXT
09/12/2020

CIA_NITT at WNUT-2020 Task 2: Classification of COVID-19 Tweets Using Pre-trained Language Models

This paper presents our models for WNUT 2020 shared task2. The shared ta...
10/16/2020

WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets

In this paper, we provide an overview of the WNUT-2020 shared task on th...
03/19/2023

COVID-19 event extraction from Twitter via extractive question answering with continuous prompts

As COVID-19 ravages the world, social media analytics could augment trad...
04/22/2018

IIIDYT at SemEval-2018 Task 3: Irony detection in English tweets

In this paper we introduce our system for the task of Irony detection in...
07/30/2020

The Unreasonable Effectiveness of Machine Learning in Moldavian versus Romanian Dialect Identification

In this work, we provide a follow-up on the Moldavian versus Romanian Cr...
01/14/2021

On Informative Tweet Identification For Tracking Mass Events

Twitter has been heavily used as an important channel for communicating ...

Please sign up or login with your details

Forgot password? Click here to reset