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        <title>ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings</title>
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        <namePart type="given">Wava</namePart>
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        <namePart type="given">Rani</namePart>
        <namePart type="given">Aulia</namePart>
        <namePart type="family">Hidayat</namePart>
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        <namePart type="given">Isnaini</namePart>
        <namePart type="given">Nurul</namePart>
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            <title>Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)</title>
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    <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</abstract>
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%0 Conference Proceedings
%T ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings
%A Putri, Wava Carissa
%A Hidayat, Rani Aulia
%A Khasanah, Isnaini Nurul
%A Mahendra, Rahmad
%Y Xu, Wei
%Y Ritter, Alan
%Y Baldwin, Tim
%Y Rahimi, Afshin
%S Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020)
%D 2020
%8 November
%I Association for Computational Linguistics
%C Online
%F putri-etal-2020-iswara
%X 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
%R 10.18653/v1/2020.wnut-1.74
%U https://aclanthology.org/2020.wnut-1.74
%U https://doi.org/10.18653/v1/2020.wnut-1.74
%P 491-494

Markdown (Informal)

[ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings](https://aclanthology.org/2020.wnut-1.74) (Putri et al., WNUT 2020)

• ISWARA at WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets using BERT and FastText Embeddings (Putri et al., WNUT 2020)

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.