Arabic Tweet-Act: Speech Act Recognition for Arabic Asynchronous Conversations

Bushra Algotiml, AbdelRahim A. Elmadany, Walid Magdy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of the Fourth Arabic Natural Language Processing Workshop
PublisherAssociation for Computational Linguistics (ACL)
Pages183-191
Number of pages9
ISBN (Electronic)978-1-950737-32-1
DOIs
Publication statusPublished - 2 Aug 2019
EventThe Fourth Arabic Natural Language Processing Workshop - Florence, Italy
Duration: 1 Aug 20192 Aug 2019
Conference number: 4
http://www.wikicfp.com/cfp/servlet/event.showcfp?eventid=83625&copyownerid=320

Workshop

WorkshopThe Fourth Arabic Natural Language Processing Workshop
Abbreviated titleWANLP 2019
CountryItaly
CityFlorence
Period1/08/192/08/19
Internet address

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