RGCL at IDAT: deep learning models for irony detection in Arabic language
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AbstractThis 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.
CitationRanasinghe, 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.
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