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.- Anthology ID:
- W19-4620
- Volume:
- Proceedings of the Fourth Arabic Natural Language Processing Workshop
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Wassim El-Hajj, Lamia Hadrich Belguith, Fethi Bougares, Walid Magdy, Imed Zitouni, Nadi Tomeh, Mahmoud El-Haj, Wajdi Zaghouani
- Venue:
- WANLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 183–191
- Language:
- URL:
- https://aclanthology.org/W19-4620
- DOI:
- 10.18653/v1/W19-4620
- Cite (ACL):
- Bushra Algotiml, AbdelRahim Elmadany, and Walid Magdy. 2019. Arabic Tweet-Act: Speech Act Recognition for Arabic Asynchronous Conversations. In Proceedings of the Fourth Arabic Natural Language Processing Workshop, pages 183–191, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- Arabic Tweet-Act: Speech Act Recognition for Arabic Asynchronous Conversations (Algotiml et al., WANLP 2019)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/W19-4620.pdf