Maghrebi Arabic dialect processing: an overview

International audienceNatural Language Processing for Arabic dialects has grown widely these last years. Indeed, several works were proposed dealing with all aspects of Natural Language Processing. However , some AD varieties have received more attention and have a growing collection of resources. Others varieties, such as Maghrebi, still lag behind in that respect. Maghrebi Arabic is the family of Arabic dialects spoken in the Maghreb region (principally Algeria, Tunisia and Morocco). In this work we are interested in these three languages. This paper presents a review of natural language processing for Maghrebi Arabic dialects

Fine-Grained Analysis of Language Varieties and Demographics

[EN] The rise of social media empowers people to interact and communicate with anyone anywhere in the world. The possibility of being anonymous avoids censorship and enables freedom of expression. Nevertheless, this anonymity might lead to cybersecurity issues, such as opinion spam, sexual harassment, incitement to hatred or even terrorism propaganda. In such cases, there is a need to know more about the anonymous users and this could be useful in several domains beyond security and forensics such as marketing, for example. In this paper, we focus on a fine-grained analysis of language varieties while considering also the authors¿ demographics. We present a Low-Dimensionality Statistical Embedding method to represent text documents. We compared the performance of this method with the best performing teams in the Author Profiling task at PAN 2017. We obtained an average accuracy of 92.08% versus 91.84% for the best performing team at PAN 2017. We also analyse the relationship of the language variety identification with the authors¿ gender. Furthermore, we applied our proposed method to a more fine-grained annotated corpus of Arabic varieties covering 22 Arab countries and obtained an overall accuracy of 88.89%. We have also investigated the effect of the authors¿ age and gender on the identification of the different Arabic varieties, as well as the effect of the corpus size on the performance of our method.This publication was made possible by NPRP grant 9-175-1-033 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors.Rangel, F.; Rosso, P.; Zaghouani, W.; Charfi, A. (2020). Fine-Grained Analysis of Language Varieties and Demographics. Natural Language Engineering. 26(6):641-661. https://doi.org/10.1017/S1351324920000108S641661266Kestemont, M. , Tschuggnall, M. , Stamatatos, E. , Daelemans, W. , Specht, G. , Stein, B. and Potthast, M. (2018). Overview of the Author Identification Task at PAN-2018: Cross-domain Authorship Attribution and Style Change Detection. CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org.McNemar, Q. (1947). Note on the sampling error of the difference between correlated proportions or percentages. Psychometrika, 12(2), 153-157. doi:10.1007/bf02295996Lui, M. and Cook, P. (2013). Classifying english documents by national dialect. In Proceedings of the Australasian Language Technology Association Workshop, Citeseer pp. 5–15.Basile, A. , Dwyer, G. , Medvedeva, M. , Rawee, J. , Haagsma, H. and Nissim, M. (2017). Is there life beyond n-grams? A simple SVM-based author profiling system. In Cappellato L., Ferro N., Goeuriot L. and Mandl T. (eds), CLEF 2017 Working Notes. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073, http://ceur-ws.org/Vol-/. CLEF and CEUR-WS.org.Elfardy, H. and Diab, M.T. (2013). Sentence level dialect identification in arabic. In Association for Computational Linguistics (ACL), pp. 456–461.Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513-523. doi:10.1016/0306-4573(88)90021-0Zaghouani, W. and Charfi, A. (2018a). ArapTweet: A large MultiDialect Twitter corpus for gender, age and language variety identification. In Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC), Miyazaki, Japan.Zampieri, M. , Tan, L. , Ljubešić, N. , Tiedemann, J. and Nakov, P. (2015). Overview of the DSL shared task 2015. In Proceedings of the Joint Workshop on Language Technology for Closely Related Languages, Varieties and Dialects, pp. 1–9.Huang, C.-R. and Lee, L.-H. (2008). Contrastive approach towards text source classification based on top-bag-of-word similarity. In PACLIC, pp. 404–410.Zaidan, O. F., & Callison-Burch, C. (2014). Arabic Dialect Identification. Computational Linguistics, 40(1), 171-202. doi:10.1162/coli_a_00169Grouin, C. , Forest, D. , Paroubek, P. and Zweigenbaum, P. (2011). Présentation et résultats du défi fouille de texte DEFT2011 Quand un article de presse a t-il été écrit? À quel article scientifique correspond ce résumé? Actes du septième Défi Fouille de Textes, p. 3.Martinc, M. , Skrjanec, I. , Zupan, K. and Pollak, S. Pan (2017). Author profiling – gender and language variety prediction. In Cappellato L., Ferro N., Goeuriot L. and Mandl T. (eds), CLEF 2017 Working Notes. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073, http://ceur-ws.org/Vol-/. CLEF and CEUR-WS.org.Rangel, F. , Rosso, P. and Franco-Salvador, M. (2016b). A low dimensionality representation for language variety identification. In 17th International Conference on Intelligent Text Processing and Computational Linguistics, CICLing, LNCS. Springer-Verlag, arxiv:1705.10754.Hagen, M. , Potthast, M. and Stein, B. (2018). Overview of the Author Obfuscation Task at PAN 2018. CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org.Zampieri, M. and Gebre, B.G. (2012). Automatic identification of language varieties: The case of portuguese. In The 11th Conference on Natural Language Processing (KONVENS), pp. 233–237 (2012)Rangel, F. , Rosso, P. , Montes-y-Gómez, M. , Potthast, M. and Stein, B. (2018). Overview of the 6th Author Profiling Task at PAN 2018: Multimodal Gender Identification in Twitter. In CLEF 2018 Labs and Workshops, Notebook Papers. CEUR Workshop Proceedings. CEUR-WS.org.Heitele, D. (1975). An epistemological view on fundamental stochastic ideas. Educational Studies in Mathematics, 6(2), 187-205. doi:10.1007/bf00302543Inches, G. and Crestani, F. (2012). Overview of the International Sexual Predator Identification Competition at PAN-2012. CLEF Online working notes/labs/workshop, vol. 30.Rosso, P. , Rangel Pardo, F.M. , Ghanem, B. and Charfi, A. (2018b). ARAP: Arabic Author Profiling Project for Cyber-Security. Sociedad Española para el Procesamiento del Lenguaje Natural (SEPLN).Agić, Ž. , Tiedemann, J. , Dobrovoljc, K. , Krek, S. , Merkler, D. , Može, S. , Nakov, P. , Osenova, P. and Vertan, C. (2014). Proceedings of the EMNLP 2014 Workshop on Language Technology for Closely Related Languages and Language Variants. Association for Computational Linguistics.Sadat, F., Kazemi, F., & Farzindar, A. (2014). Automatic Identification of Arabic Language Varieties and Dialects in Social Media. Proceedings of the Second Workshop on Natural Language Processing for Social Media (SocialNLP). doi:10.3115/v1/w14-5904Franco-Salvador, M., Rangel, F., Rosso, P., Taulé, M., & Antònia Martít, M. (2015). Language Variety Identification Using Distributed Representations of Words and Documents. Experimental IR Meets Multilinguality, Multimodality, and Interaction, 28-40. doi:10.1007/978-3-319-24027-5_3Rosso, P., Rangel, F., Farías, I. H., Cagnina, L., Zaghouani, W., & Charfi, A. (2018). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass, 12(4), e12275. doi:10.1111/lnc3.12275Malmasi, S. , Zampieri, M. , Ljubešić, N. , Nakov, P. , Ali, A. and Tiedemann, J. (2016). Discriminating between similar languages and arabic dialect identification: A report on the third DSL shared task. In Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial3), pp. 1–14.Rangel, F. , Rosso, P. , Potthast, M. and Stein, B. (2017). Overview of the 5th Author Profiling Task at PAN 2017: Gender and Language Variety Identification in Twitter. In Cappellato L., Ferro N., Goeuriot, L. and Mandl T. (eds), Working Notes Papers of the CLEF 2017 Evaluation Labs, p. 1613–0073, CLEF and CEUR-WS.org.Zampieri, M. , Malmasi, S. , Ljubešić, N. , Nakov, P. , Ali, A. , Tiedemann, J. , Scherrer, Y. , Aepli, N. (2017). Findings of the vardial evaluation campaign 2017. In Proceedings of the Fourth Workshop on NLP for Similar Languages, Varieties and Dialects, pp. 1–15.Bogdanova, D., Rosso, P., & Solorio, T. (2014). Exploring high-level features for detecting cyberpedophilia. Computer Speech & Language, 28(1), 108-120. doi:10.1016/j.csl.2013.04.007Maier, W. and Gómez-Rodríguez, C. (2014). Language Variety Identification in Spanish Tweets. LT4CloseLang.Castro, D. , Souza, E. , de Oliveira, A.L.I. (2016). Discriminating between Brazilian and European Portuguese national varieties on Twitter texts. In 5th Brazilian Conference on Intelligent Systems (BRACIS), pp. 265–270.Zaghouani, W. and Charfi, A. (2018b). Guidelines and annotation framework for Arabic author profiling. In Proceedings of the 3rd Workshop on Open-Source Arabic Corpora and Processing Tools, 11th International Conference on Language Resources and Evaluation (LREC), Miyazaki, Japan.Hernández Fusilier, D., Montes-y-Gómez, M., Rosso, P., & Guzmán Cabrera, R. (2015). Detecting positive and negative deceptive opinions using PU-learning. Information Processing & Management, 51(4), 433-443. doi:10.1016/j.ipm.2014.11.001Tellez, E.S. , Miranda-Jiménez, S. , Graff, M. and Moctezuma, D. (2017). Gender and language variety identification with microtc. In Cappellato L., Ferro N., Goeuriot L. and Mandl T. (eds). CLEF 2017 Working Notes. CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613-0073, http://ceur-ws.org/Vol-/. CLEF and CEUR-WS.org.Kandias, M., Stavrou, V., Bozovic, N., & Gritzalis, D. (2013). Proactive insider threat detection through social media. Proceedings of the 12th ACM workshop on Workshop on privacy in the electronic society. doi:10.1145/2517840.251786

A survey on author profiling, deception, and irony detection for the Arabic language

"This is the peer reviewed version of the following article: [FULL CITE], which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving."[EN] The possibility of knowing people traits on the basis of what they write is a field of growing interest named author profiling. To infer a user's gender, age, native language, language variety, or even when the user lies, simply by analyzing her texts, opens a wide range of possibilities from the point of view of security. In this paper, we review the state of the art about some of the main author profiling problems, as well as deception and irony detection, especially focusing on the Arabic language.Qatar National Research Fund, Grant/Award Number: NPRP 9-175-1-033Rosso, P.; Rangel-Pardo, FM.; Hernandez-Farias, DI.; Cagnina, L.; Zaghouani, W.; Charfi, A. (2018). A survey on author profiling, deception, and irony detection for the Arabic language. Language and Linguistics Compass. 12(4):1-20. https://doi.org/10.1111/lnc3.12275S120124Abuhakema , G. Faraj , R. Feldman , A. Fitzpatrick , E. 2008 Annotating an arabic learner corpus for error Proceedings of The sixth international conference on Language Resources and Evaluation, LREC 2008Adouane , W. Dobnik , S. 2017 Identification of languages in algerian arabic multilingual documents Proceedings of The Third Arabic Natural Language Processing Workshop (WANLP)Adouane , W. Semmar , N. Johansson , R 2016a Romanized berber and romanized arabic automatic language identification using machine learning Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 53 61Adouane , W. Semmar , N. Johansson , R. 2016b ASIREM participation at the discriminating similar languages shared task 2016 Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 163 169Adouane , W. Semmar , N. Johansson , R. Bobicev , V. 2016c Automatic detection of arabicized berber and arabic varieties Proceedings of the Third Workshop on NLP for Similar Languages, Varieties and Dialects; COLING 63 72Alfaifi , A. Atwell , E. Hedaya , I. 2014 Arabic learner corpus (ALC) v2: A new written and spoken corpus of Arabic learnersAlharbi , K. 2015 The irony volcano explodes black comedyAli , A. Bell , P. Renals , S. 2015 Automatic dialect detection in Arabic broadcast speechAlmeman , K. Lee , M. 2013 Automatic building of Arabic multi dialect text corpora by bootstrapping dialect words 1 6Aloshban , N. Al-Dossari , H. 2016 A new approach for group spam detection in social media for Arabic language (AGSD) 20 23Al-Sabbagh , R. Girju , R. 2012 YADAC: Yet another dialectal Arabic corpusAlsmearat , K. Al-Ayyoub , M. Al-Shalabi , R. 2014 An extensive study of the bag-of-words approach for gender identification of Arabic articlesAlsmearat , K. Shehab , M. Al-Ayyoub , M. Al-Shalabi , R. Kanaan , G. 2015 Emotion analysis of Arabic articles and its impact on identifying the authors genderArfath , P. Al-Badrashiny , M. Diab , M. El Kholy , A. Eskander , R. Habash , N. Pooleery , M. Rambow , O. Roth , R. M. 2014 MADAMIRA: A fast, comprehensive tool for morphological analysis and disambiguation of ArabicBarbieri , F. Basile , V. Croce , D. Nissim , M. Novielli , N. Patti , V. 2016 Overview of the Evalita 2016 sentiment polarity classification taskBarbieri , F. Saggion , H 2014 Modelling irony in twitter 56 64Barbieri , F. Saggion , H. Ronzano , F 2014 Modelling sarcasm in Twitter, a novel approachBasile , V. Bolioli , A. Nissim , M. Patti , V. Rosso , P. 2014 Overview of the Evalita 2014 sentiment polarity classification taskBlanchard, D., Tetreault, J., Higgins, D., Cahill, A., & Chodorow, M. (2013). TOEFL11: A CORPUS OF NON-NATIVE ENGLISH. ETS Research Report Series, 2013(2), i-15. doi:10.1002/j.2333-8504.2013.tb02331.xBosco, C., Patti, V., & Bolioli, A. (2013). Developing Corpora for Sentiment Analysis: The Case of Irony and Senti-TUT. IEEE Intelligent Systems, 28(2), 55-63. doi:10.1109/mis.2013.28Bouamor , H. Habash , N. Salameh , M. Zaghouani , W. Rambow , O. Abdulrahim , D. Oflazer , K. 2018 The MADAR Arabic Dialect Corpus and LexiconBouchlaghem , R. Elkhlifi , A. Faiz , R. 2014 Tunisian dialect Wordnet creation and enrichment using web resources and other Wordnets 104 113 https://doi.org/10.3115/v1/W14-3613Boujelbane , R. BenAyed , S. Belguith , L. H. 2013 Building bilingual lexicon to create dialect Tunisian corpora and adapt language modelCagnina L. Rosso , P 2015 Classification of deceptive opinions using a low dimensionality representationCavalli-Sforza , V. Saddiki , H. Bouzoubaa , K. Abouenour , L. Maamouri , M. Goshey , E. 2013 Bootstrapping a Wordnet for an Arabic dialect from other Wordnets and dictionary resourcesCotterell , R. Callison-Burch , C. 2014 A multi-dialect, multi-genre corpus of informal written ArabicDahlmeier , D. Tou Ng , H. Mei Wu , S. 2013 Building a large annotated corpus of learner English: the NUS corpus of learner English 22 31Darwish , K. Sajjad , H. Mubarak , H. 2014 Verifiably effective Arabic dialect identification 1465 1468Duh , K. Kirchhoff , K. 2006 Lexicon acquisition for dialectal Arabic using transductive learningElfardy , E. Diab , M. T. 2013 Sentence level dialect identification in Arabic 456 461Estival , D. Gaustad , T. Hutchinson , B. Bao-Pham , S. Radford , W. 2008 Author profiling for English and Arabic emailsFitzpatrick, E., Bachenko, J., & Fornaciari, T. (2015). Automatic Detection of Verbal Deception. Synthesis Lectures on Human Language Technologies, 8(3), 1-119. doi:10.2200/s00656ed1v01y201507hlt029Franco-Salvador, M., Rangel, F., Rosso, P., Taulé, M., & Antònia Martít, M. (2015). Language Variety Identification Using Distributed Representations of Words and Documents. Experimental IR Meets Multilinguality, Multimodality, and Interaction, 28-40. doi:10.1007/978-3-319-24027-5_3Ghosh , A. Li , G. Veale , T. Rosso , P. Shutova , E. Barnden , J. Reyes , A. 2015 Semeval-2015 task 11: Sentiment analysis of figurative language in twitter 470 478Graff , D. Maamouri , M. 2012 Developing LMF-XML bilingual dictionaries for colloquial Arabic dialects 269 274Habash , N. Khalifa , S. Eryani , F. Rambow , O. Abdulrahim , D. Erdmann , A. Saddiki , H. 2018 Unified Guidelines and Resources for Arabic Dialect OrthographyHabash , N. Rambow , O. Kiraz , G. 2005 Morphological analysis and generation for Arabic dialectsHaggan, M. (1991). Spelling errors in native Arabic-speaking English majors: A comparison between remedial students and fourth year students. System, 19(1-2), 45-61. doi:10.1016/0346-251x(91)90007-cHassan , H. Daud , N. M. 2011 Corpus analysis of conjunctions: Arabic learners difficulties with collocationsHayes-Harb, R. (2006). Native Speakers of Arabic and ESL Texts: Evidence for the Transfer of Written Word Identification Processes. TESOL Quarterly, 40(2), 321. doi:10.2307/40264525Hernández-Farías, I., Benedí, J.-M., & Rosso, P. (2015). Applying Basic Features from Sentiment Analysis for Automatic Irony Detection. Lecture Notes in Computer Science, 337-344. doi:10.1007/978-3-319-19390-8_38Hernández Fusilier, D., Montes-y-Gómez, M., Rosso, P., & Guzmán Cabrera, R. (2015). Detecting positive and negative deceptive opinions using PU-learning. Information Processing & Management, 51(4), 433-443. doi:10.1016/j.ipm.2014.11.001Karoui , J. Benamara , F. Moriceau , V. Aussenac-Gilles , N. Hadrich Belguith , L. 2015 Towards a contextual pragmatic model to detect irony in tweetsKaroui , J. Zitoune , F. B. Moriceau , V. 2017 SOUKHRIA: Towards an irony detection system for Arabic in social mediaLjubesic , N. Mikelic , N. Boras , D. 2007 Language identification: How to distinguish similar languagesLópez-Monroy, A. P., Montes-y-Gómez, M., Escalante, H. J., Villaseñor-Pineda, L., & Stamatatos, E. (2015). Discriminative subprofile-specific representations for author profiling in social media. Knowledge-Based Systems, 89, 134-147. doi:10.1016/j.knosys.2015.06.024Magdy, W., Darwish, K., & Weber, I. (2016). #FailedRevolutions: Using Twitter to study the antecedents of ISIS support. First Monday. doi:10.5210/fm.v21i2.6372Maier , W. Gomez-Rodriguez , C. 2014 Language variety identification in Spanish tweetsMalmasi , S. Dras , M. 2014 Arabic native language identificationMechti , S. Abbassi , A. Belguith , L. H. Faiz , R. 2016 An empirical method using features combination for Arabic native language identificationMukherjee, A., Liu, B., & Glance, N. (2012). Spotting fake reviewer groups in consumer reviews. Proceedings of the 21st international conference on World Wide Web - WWW ’12. doi:10.1145/2187836.2187863Proceedings of the EMNLP’2014 Workshop on Language Technology for Closely Related Languages and Language Variants. (2014). doi:10.3115/v1/w14-42Pennebaker , J. W. Chung , C. K. Ireland , M. E. Gonzales , A. L. Booth , R. J. 2007 The development and psychometric properties of LIWC2007 http://www.liwc.net/LIWC2007LanguageManual.pdf http://liwc.netPotthast , M. Rangel , F. Tschuggnall , M. Stamatatos , E. Rosso , P. Stein , B. 2017 Overview of PAN'17 G. Jones 10456 Springer, ChamRandall M. Groom , N. 2009 The BUiD Arab learner corpus: a resource for studying the acquisition of l2 English spellingRangel , F. Rosso , P. 2015 On the multilingual and genre robustness of emographs for author profiling in social media 274 280 Springer-Verlag, LNCSRangel, F., & Rosso, P. (2016). On the impact of emotions on author profiling. Information Processing & Management, 52(1), 73-92. doi:10.1016/j.ipm.2015.06.003Rangel , F. Rosso , P. Koppel , M. Stamatatos , E. Inches , G. 2013 Overview of the author profiling task at PAN 2013 P. Forner R. Navigli D. TufisRangel , F. Rosso , P. Potthast , M. Stein , B. Daelemans , W. 2015 Overview of the 3rd author profiling task at PAN 2015 L. Cappellato N. Ferro G. Jones E. San JuanRangel , F. Rosso , P. Verhoeven , B. Daelemans , W. Potthast , M. Stein , B. 2016 Overview of the 4th author profiling task at PAN 2016: Cross-genre evaluationsRefaee , E. Rieser , V. 2014 An Arabic twitter corpus for subjectivity and sentiment analysis 2268 2273Reyes, A., Rosso, P., & Buscaldi, D. (2012). From humor recognition to irony detection: The figurative language of social media. Data & Knowledge Engineering, 74, 1-12. doi:10.1016/j.datak.2012.02.005Reyes, A., Rosso, P., & Veale, T. (2012). A multidimensional approach for detecting irony in Twitter. Language Resources and Evaluation, 47(1), 239-268. doi:10.1007/s10579-012-9196-xRosso, P., & Cagnina, L. C. (2017). Deception Detection and Opinion Spam. Socio-Affective Computing, 155-171. doi:10.1007/978-3-319-55394-8_8Saâdane , H. 2015 Traitement Automatique de L'Arabe Dialectalise: Aspects Methodologiques et AlgorithmiquesSaâdane , H. Nouvel , D. Seffih , H. Fluhr , C. 2017 Une approche linguistique pour la détection des dialectes arabesSadat , F. Kazemi , F. Farzindar , A. 2014 Automatic identification of Arabic language varieties and dialects in social mediaSadhwani , P. 2005 Phonological and orthographic knowledge: An Arab-Emirati perspectiveSchler , J. Koppel , M. Argamon , S. Pennebaker , J. W. 2006 Effects of age and gender on blogging 199 205Shoufan , A. Al-Ameri , S. 2015 Natural language processing for dialectical Arabic: A surveySoliman , T. Elmasry , M. Hedar , A-R. Doss , M. 2013 MINING SOCIAL NETWORKS' ARABIC SLANG COMMENTSSulis, E., Irazú Hernández Farías, D., Rosso, P., Patti, V., & Ruffo, G. (2016). Figurative messages and affect in Twitter: Differences between #irony, #sarcasm and #not. Knowledge-Based Systems, 108, 132-143. doi:10.1016/j.knosys.2016.05.035Tetreault , J. Blanchard , D. Cahill , A. 2013 A report on the first native language identification shared task Proceedings of the 8th Workshop on Innovative Use of NLP for Building Educational Applications 48 57Tillmann , C. Mansour , S. Al Onaizan , Y. 2014 Improved sentence-level Arabic dialect classification Proceedings of the VarDia006C Workshop 110 119Tono, Y. (2012). International Corpus of Crosslinguistic Interlanguage: Project overview and a case study on the acquisition of new verb co-occurrence patterns. Tokyo University of Foreign Studies, 27-46. doi:10.1075/tufs.4.07tonWahsheh , H. A. Al-Kabi , M. N. Alsmadi , I. M. 2013b SPAR: A system to detect spam in Arabic opinionsZaghouani , W. Charfi , A. 2018a Arap-Tweet: A Large Multi-Dialect Twitter Corpus for Gender, Age and Language Variety Identification Miyazaki, JapanZaghouani , W. Charfi , A. 2018b Guidelines and Annotation Framework for Arabic Author Profiling Miyazaki, JapanZaghouani , W. Mohit , B. Habash , N. Obeid , O. Tomeh , N. Rozovskaya , A. Farra , N. Alkuhlani , S. Oflazer , K. 2014 Large scale Arabic error annotation: Guidelines and frameworkZaghouani , W. Habash , N. Bouamor , H. Rozovskaya , A. Mohit , B. Heider , A. Oflazer , K. 2015 Correction annotation for non-native Arabic texts: Guidelines and corpus Proceedings of the Association for Computational Linguistics, Fourth Linguistic Annotation Workshop 129 139Zaidan , O. F. Callison-Burch , C 2011 The Arabic online commentary dataset: An annotated dataset of informal Arabic with high dialectal content Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers -Volume 2 Association for Computational Linguistics 37 41Zaidan, O. F., & Callison-Burch, C. (2014). Arabic Dialect Identification. Computational Linguistics, 40(1), 171-202. doi:10.1162/coli_a_00169Zampieri , M. Gebre , B. G. 2012 Automatic identification of language varieties: The case of PortugueseZampieri , M. Tan , L. Ljubesic , N. Tiedemann , J. 2014 A report on the DSL shared task 2014 Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects 58 67Zampieri , M. Tan , L. Ljubesic , N. Tiedemann , J. Nakov , P. 2015 Overview of the DSL shared task 2015 1Zbib , R. Malchiodi , E. Devlin , J. Stallard , D. Matsoukas , S. Schwartz , R. Makhoul , J. Zaidan , O. F. Callison Burch , C. 2012 Machine translation of Arabic dialects Proceedings of the 2012 conference of the North American chapter of the Association for Computational Linguistics: Human language technologies Association for Computational Linguistics 49 5

ArAutoSenti: Automatic annotation and new tendencies for sentiment classification of Arabic messages

The file attached to this record is the author's final peer reviewed version.A corpus-based sentiment analysis approach for messages written in Arabic and its dialects is presented and implemented. The originality of this approach resides in the automation construction of the annotated sentiment corpus, which relies mainly on a sentiment lexicon that is also constructed automatically. For the classification step, shallow and deep classifiers are used with features being extracted applying word embedding models. For the validation of the constructed corpus, we proceed with a manual reviewing and it was found that 85.17% were correctly annotated. This approach is applied on the under-resourced Algerian dialect and the approach is tested on two external test corpora presented in the literature. The obtained results are very encouraging with an F1-score that is up to 88% (on the first test corpus) and up to 81% (on the second test corpus). These results respectively represent a 20% and a 6% improvement, respectively, when compared with existing work in the research literature

Computational modelling of segmental and prosodic levels of analysis for capturing variation across Arabic dialects

Dialect variation spans different linguistic levels of analysis. Two examples include the typical phonetic realisations produced and the typical range of intonational choices made by individuals belonging to a given dialect group. Taking the modelling principles of a specific automatic accent recognition system, the work here characterises and observes the variation that exists within these two specific levels of analysis among eight Arabic dialects. Using a method that has previously shown promising performance on English accent varieties, we first model the segmental level of analysis from recordings of Arabic speakers to capture the variation in the phonetic realisations of the vowels and consonants. In doing so, we show how powerful this model can be in distinguishing between Arabic dialects. This paper then shows how this modelling approach can be adapted to instead characterise prosodic variation among these same dialects from the same speech recordings. This allows us to inspect the relative power of the segmental and prosodic levels of analysis in separating the Arabic dialects. This work opens up the possibility of using these modelling frameworks to study the extent and nature of phonetic and prosodic variation across speech corpora

The Status and Future of Arabic Use amid Colonial Languages in the Arab World in Times of Globalization and Advanced Technology: A Political and Sociolinguistic Approach

This paper studies the languages and cultures that are commonly present in individual and collective practices of the Arab world, to understand the extent to which the identity and the linguistic approach in the region are affected by the historical and geographical context. It analyzes the status of Arabic and its future amid colonial languages, such as English and French, in the Arab world, specifically Morocco and Lebanon as case studies, in times of globalization and advanced technology. In a large and diversified arena, such as the Arab world, there is often a strong commitment and devotion to languages, such as Arabic, French, and English. Accordingly, this study further examines how Arabic can strengthen its practice and protect its status in an environment dominated by colonial languages. In this regard, the future of the Arabic language in the current ever-changing sociolinguistic context is subject to questioning and concerns for its official forthcoming evolution. Globalization, internet, social networks, digital technologies, and fast communication are no longer enabling linguistic authorities to provide a clear and accurate vision on the future of this language. Thus, addressing the question of languages in the future pushes researchers to consider and analyze the several linguistic strategies and policies implemented by the concerned authorities who are building and promoting an overall representation within and outside their original area

PADIC: extension and new experiments

International audiencePADIC is a multidialectal parallel Arabic corpus. It was composed initially by five Arabic dialects, three from the Maghreb and two from the Middle East, in addition to standard Arabic. In this paper, we present an augmented version of PADIC with a Moroccan dialect. We give also an evaluation, using the σ–index, of the computerization level of the Arabic dialects present in PADIC which reveals that these languages are really under-resourced. Several experiments in machine translation, in both sides between all the combinations of language pairs, are discussed too. For each language, we interpolated the corresponding Language Model (LM) with a large Arabic corpus based LM. The results show that this interpolation is in some cases without effect on the performances of translation systems and in others is rather penalizing