Work

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

Year: 2020

Type: conference-paper

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.

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

DOI: 10.18653/v1/2020.wnut-1.74

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

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W3098683905

FWCI: 0.095

Cites: 13

Cited by: 1

Related to: 10