ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings

Wava Carissa Putri, Rani Aulia Hidayat, Isnaini Nurul Khasanah, Rahmad Mahendra


Abstract
This paper presents Iswara’s participation in the WNUT-2020 Task 2 “Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings”,which tries to classify whether a certain tweet is considered informative or not. We proposed a method that utilizes word embeddings and using word occurrence related to the topic for this task. We compare several models to get the best performance. Results show that pairing BERT with word occurrences outperforms fastText with F1-Score, precision, recall, and accuracy on test data of 76%, 81%, 72%, and 79%, respectively
Anthology ID:
2020.wnut-1.74
Volume:
Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
Month:
November
Year:
2020
Address:
Online
Editors:
Wei Xu, Alan Ritter, Tim Baldwin, Afshin Rahimi
Venue:
WNUT
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
491–494
Language:
URL:
https://aclanthology.org/2020.wnut-1.74
DOI:
10.18653/v1/2020.wnut-1.74
Bibkey:
Cite (ACL):
Wava Carissa Putri, Rani Aulia Hidayat, Isnaini Nurul Khasanah, and Rahmad Mahendra. 2020. ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings. In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020), pages 491–494, Online. Association for Computational Linguistics.
Cite (Informal):
ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings (Putri et al., WNUT 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.wnut-1.74.pdf
Data
WNUT-2020 Task 2