RGCL at IDAT: deep learning models for irony detection in Arabic language
Authors
Ranasinghe, TharinduSaadany, Hadeel
Plum, Alistair
Mandhari, Salim
Mohamed, Emad
Orasan, Constantin
Mitkov, Ruslan
Issue Date
2019-12-12
Metadata
Show full item recordAbstract
This article describes the system submitted by the RGCL team to the IDAT 2019 Shared Task: Irony Detection in Arabic Tweets. The system detects irony in Arabic tweets using deep learning. The paper evaluates the performance of several deep learning models, as well as how text cleaning and text pre-processing influence the accuracy of the system. Several runs were submitted. The highest F1 score achieved for one of the submissions was 0.818 making the team RGCL rank 4th out of 10 teams in final results. Overall, we present a system that uses minimal pre-processing but capable of achieving competitive results.Citation
Ranasinghe, T. et al.(2019) RGCL at IDAT: deep learning models for irony detection in Arabic language, in Metha, P., Rosso, P., Majumder, P. and Mitra, M. (eds.) Working Notes of FIRE 2019 - Forum for Information Retrieval Evaluation, Kolkata, India, 12th-15th December, 2019. CEUR Workshop Proceedings Volume 2517, 2019, Pages 416-425.Publisher
IDATAdditional Links
http://irlab.daiict.ac.in/~Parth/T4-5.pdfType
Conference contributionLanguage
enISSN
1613-0073
Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by/4.0/