@inproceedings{algotiml-etal-2019-arabic,
title = "{A}rabic Tweet-Act: Speech Act Recognition for {A}rabic Asynchronous Conversations",
author = "Algotiml, Bushra and
Elmadany, AbdelRahim and
Magdy, Walid",
editor = "El-Hajj, Wassim and
Belguith, Lamia Hadrich and
Bougares, Fethi and
Magdy, Walid and
Zitouni, Imed and
Tomeh, Nadi and
El-Haj, Mahmoud and
Zaghouani, Wajdi",
booktitle = "Proceedings of the Fourth Arabic Natural Language Processing Workshop",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-4620",
doi = "10.18653/v1/W19-4620",
pages = "183--191",
abstract = "Speech acts are the actions that a speaker intends when performing an utterance within conversations. In this paper, we proposed speech act classification for asynchronous conversations on Twitter using multiple machine learning methods including SVM and deep neural networks. We applied the proposed methods on the ArSAS tweets dataset. The obtained results show that superiority of deep learning methods compared to SVMs, where Bi-LSTM managed to achieve an accuracy of 87.5{\%} and a macro-averaged F1 score 61.5{\%}. We believe that our results are the first to be reported on the task of speech-act recognition for asynchronous conversations on Arabic Twitter.",
}
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<abstract>Speech acts are the actions that a speaker intends when performing an utterance within conversations. In this paper, we proposed speech act classification for asynchronous conversations on Twitter using multiple machine learning methods including SVM and deep neural networks. We applied the proposed methods on the ArSAS tweets dataset. The obtained results show that superiority of deep learning methods compared to SVMs, where Bi-LSTM managed to achieve an accuracy of 87.5% and a macro-averaged F1 score 61.5%. We believe that our results are the first to be reported on the task of speech-act recognition for asynchronous conversations on Arabic Twitter.</abstract>
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%0 Conference Proceedings
%T Arabic Tweet-Act: Speech Act Recognition for Arabic Asynchronous Conversations
%A Algotiml, Bushra
%A Elmadany, AbdelRahim
%A Magdy, Walid
%Y El-Hajj, Wassim
%Y Belguith, Lamia Hadrich
%Y Bougares, Fethi
%Y Magdy, Walid
%Y Zitouni, Imed
%Y Tomeh, Nadi
%Y El-Haj, Mahmoud
%Y Zaghouani, Wajdi
%S Proceedings of the Fourth Arabic Natural Language Processing Workshop
%D 2019
%8 August
%I Association for Computational Linguistics
%C Florence, Italy
%F algotiml-etal-2019-arabic
%X Speech acts are the actions that a speaker intends when performing an utterance within conversations. In this paper, we proposed speech act classification for asynchronous conversations on Twitter using multiple machine learning methods including SVM and deep neural networks. We applied the proposed methods on the ArSAS tweets dataset. The obtained results show that superiority of deep learning methods compared to SVMs, where Bi-LSTM managed to achieve an accuracy of 87.5% and a macro-averaged F1 score 61.5%. We believe that our results are the first to be reported on the task of speech-act recognition for asynchronous conversations on Arabic Twitter.
%R 10.18653/v1/W19-4620
%U https://aclanthology.org/W19-4620
%U https://doi.org/10.18653/v1/W19-4620
%P 183-191
Markdown (Informal)
[Arabic Tweet-Act: Speech Act Recognition for Arabic Asynchronous Conversations](https://aclanthology.org/W19-4620) (Algotiml et al., WANLP 2019)
ACL