Author Profiling and Plagiarism Detection

The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-25485-2_6In this chapter we introduce the topics that we will cover in the RuSSIR 2014 course on Author Profiling and Plagiarism Detection (APPD). Author profiling distinguishes between classes of authors studying how language is shared by classes of people. This task helps in identifying profiling aspects such as gender, age, native language, or even personality type. In case of the plagiarism detection task we are not interested in studying how language is shared. On the contrary, given a document we are interested in investigating if the writing style changes in order to unveil text inconsistencies, i.e., unexpected irregularities through the document such as changes in vocabulary, style and text complexity. In fact, when it is not possible to retrieve the source document(s) where plagiarism has been committed from, the intrinsic analysis of the suspicious document is the only way to find evidence of plagiarism. The difficulty in retrieving the source of plagiarism could be due to the fact that the documents are not available on the web or the plagiarised text fragments were obfuscated via paraphrasing or translation (in case the source document was in another language). In this overview, we also discuss the results of the shared tasks on author profiling (gender and age identification) and plagiarism detection that we help to organise at the PAN Lab on Uncovering Plagiarism, Authorship, and Social Software Misuse.The PAN shared tasks on author profil-ing and on plagiarism detection have been organised in the framework of the WIQ-EIIRSES project (Grant No. 269180) within the EC FP 7 Marie Curie People. The research work described in the paper was carried out in the framework of the DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction inIntelligent Systems.Rosso, P. (2015). Author Profiling and Plagiarism Detection. En Information Retrieval. Springer. 229-250. https://doi.org/10.1007/978-3-319-25485-2_6S229250Argamon, S., Koppel, M., Fine, J., Shimoni, A.R.: Gender, genre, and writing style in formal written texts. TEXT 23, 321–346 (2003)Association of Teachers and Lecturers. School work plagued by plagiarism - ATL survey. Technical report, Association of Teachers and Lecturers, London, UK (2008). (Press release)Barrón-Cedeño, A.: On the mono- and cross-language detection of text re-use and plagiarism. Ph.D. thesis, Universitat Politènica de València (2012)Barrón-Cedeño, A., Rosso, P., Pinto, D., Juan, A.: On cross-lingual plagiarism analysis using a statistical model. In: Proceedings of the ECAI 2008 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, PAN 2008 (2008)Barrón-Cedeño, A., Gupta, P., Rosso, P.: Methods for cross-language plagiarism detection. Knowl. Based Syst. 50, 11–17 (2013)Barrón-Cedeño, A., Vila, M., Martí, M., Rosso, P.: Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection. Comput. Linguist. 39(4), 917–947 (2013)Bogdanova, D., Rosso, P., Solorio, T.: Exploring high-level features for detecting cyberpedophilia. Comput. Speech Lang. 28(1), 108–120 (2014)Braschler, M., Harman, D.: Notebook papers of CLEF 2010 LABs and workshops. Padua, Italy (2010)Cappellato, L., Ferro, N., Halvey, M., Kraaij, W.: CLEF 2014 labs and workshops, notebook papers. In: CEUR Workshop Proceedings (CEUR-WS.org), ISSN 1613–0073 (2014). http://ceur-ws.org/Vol-1180/Comas, R., Sureda, J., Nava, C., Serrano, L.: Academic cyberplagiarism: a descriptive and comparative analysis of the prevalence amongst the undergraduate students at Tecmilenio University (Mexico) and Balearic Islands University (Spain). In: Proceedings of the International Conference on Education and New Learning Technologies (EDULEARN 2010), Barcelona (2010)Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32(3), 221–233 (1948)Flores, E., Barrón-Cedeño, A., Rosso, P., Moreno, L.: Desocore: detecting source code re-use across programming languages. In: Proceedings of 12th International Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-2012, pp. 1–4, Montreal, Canada (2012)Flores, E., Barrón-Cedeño, A., Moreno, L., Rosso, P.: Uncovering source code re-use in large-scale programming environments. In: Computer Applications in Engineering and Education, Accepted (2014). doi: 10.1002/cae.21608Forner, P., Navigli, R., Tufis, D.: CLEF 2013 evaluation labs and workshop - working notes papers, 23–26 September. Valencia, Spain (2013)Franco-Salvador, M., Gupta, P., Rosso, P.: Cross-Language plagiarism detection using a multilingual semantic network. In: Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E., Serdyukov, P. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 710–713. Springer, Heidelberg (2013)Franco-Salvador, M., Gupta, P., Rosso, P.: Knowledge graphs as context models: improving the detection of cross-language plagiarism with paraphrasing. In: Ferro, N. (ed.) PROMISE Winter School 2013. LNCS, vol. 8173, pp. 227–236. Springer, Heidelberg (2014)Gollub, T., Stein, B., Burrows, S.: Ousting Ivory tower research: towards a web framework for providing experiments as a service. In: Hersh, B., Callan, J., Maarek, Y., Sanderson, M., (eds.) 35th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2012), pp. 1125–1126. ACM, August 2012. ISBN 978-1-4503-1472-5. doi: 10.1145/2348283.2348501Gollub, T., Hagen, M., Michel, M., Stein, B.: From keywords to keyqueries: content descriptors for the web. In: Gurrin, C., Jones, G., Kelly, D., Kruschwitz, U., de Rijke, M., Sakai, T., Sheridan, P., (eds.) 36th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2013), pp. 981–984. ACM (2013)Goswami, S., Sarkar, S., Rustagi, M.: Stylometric analysis of bloggers’ age and gender. In: Adar, E., Hurst, M., Finin, T., Glance, N.S., Nicolov, N., Tseng, B.L., (eds.) ICWSM. The AAAI Press (2009)Gressel, G., Hrudya, P., Surendran, K., Thara, S., Aravind, A., Prabaharan, P.: Ensemble Learning Approach for Author Profiling-Notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Grozea, C., Popescu, M.: ENCOPLOT - performance in the Second International Plagiarism Detection Challenge lab report for PAN at CLEF 2010. In: Braschler and Harman [8]Grozea, C., Gehl, C., Popescu, M.: ENCOPLOT: pairwise sequence matching in linear time applied to plagiarism detection. In: Stein et al., (ed.) Overview of the 1st International Competition on Plagiarism Detection, pp. 10–18 (2009)Gunning, R.: The Technique of Clear Writing. McGraw-Hill Int. Book Co, New York (1952)Gupta, P., Barrón-Cedeño, A., Rosso, P.: Cross-language high similarity search using a conceptual thesaurus. In: Catarci, T., Peñas, A., Santucci, G., Forner, P., Hiemstra, D. (eds.) CLEF 2012. LNCS, vol. 7488, pp. 67–75. Springer, Heidelberg (2012)Honore, A.: Some simple measures of richness of vocabulary. Assoc. Lit. Linguist. Comput. Bull. 7(2), 172–177 (1979)IEEE. A Plagiarism FAQ. http://www.ieee.org/publications_standards/publications/rights/plagiarism_FAQ.html (2008). Published: 2008; Last Accessed 25 November 2012Koppel, M., Argamon, S., Shimoni, A.R.: Automatically categorizing written texts by author gender. Lit. Linguist. Comput. 17(4), 401–412 (2002)Liau, Y., Vrizlynn, L.: Submission to the author profiling competition at pan-2014. In: Proceedings Recent Advances in Natural Language Processing III (2014). http://www.webis.de/research/events/pan-14Lopez-Monroy, A.P., Montes-Y-Gomez, M., Escalante, H.J., Villaseñor-Pineda, L., Villatoro-Tello, E.: INAOE’s participation at PAN 2013: author profiling task–notebook for PAN at CLEF 2013. In: Forner, et al. [14]Pastor López-Monroy, A., Montes y Gómez, M., Escalante, H.J., Villaseñor-Pineda, L.: Using Intra-profile information for author profiling-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Maharjan, S., Shrestha, P., Solorio, T.: A simple approach to author profiling in MapReduce–notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Marquardt, J., Fanardi, G., Vasudevan, G., Moens, M.F., Davalos, S., Teredesai, A., De Cock, M.: Age and gender identification in social media-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Martin, B.: Plagiarism: policy against cheating or policy for learning? Nexus (Newsl. Aust. Sociol. Assoc.) 16(2), 15–16 (2004)Mcnamee, P., Mayfield, J.: Character n-gram tokenization for european language text retrieval. Inf. Retr. 7(1), 73–97 (2004)Meina, M., Brodzinska, K., Celmer, B., Czokow, M., Patera, M., Pezacki, J., Wilk, M.: Ensemble-based classification for author profiling using various features-notebook for PAN at CLEF 2013. In: Forner, et al. [14]Eissen, S.M., Stein, B.: Intrinsic plagiarism detection. In: Tombros, A., Yavlinsky, A., Rüger, S.M., Tsikrika, T., Lalmas, M., MacFarlane, A. (eds.) ECIR 2006. LNCS, vol. 3936, pp. 565–569. Springer, Heidelberg (2006)Montes y Gómez, M., Gelbukh, A.F., López-López, A., Baeza-Yates, R.A.: Flexible comparison of conceptual graphs. In: Proceedings DEXA, pp. 102–111 (2001)Navigli, R., Ponzetto, S.P.: BabelNet: the automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artif. Intell. 193, 217–250 (2012)Nawab, R.M.A., Stevenson, M., Clough, P.: University of sheffield lab report for pan at clef 2010. In: Braschler and Harman [8]Nguyen, D., Gravel, R., Trieschnigg, D., Meder, T.: “how old do you think i am?”; a study of language and age in twitter. In: Proceedings of the Seventh International AAAI Conference on Weblogs and Social Media (2013)Oberreuter, G., Eiselt, A.: Submission to the 6th international competition on plagiarism detection, From Innovand.io, Chile (2014). http://www.webis.de/research/events/pan-14Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Comput. Linguist. 29(1), 19–51 (2003)Palkovskii, Y., Belov, A.: Developing high-resolution universal multi-type N-Gram plagiarism detector-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Pennebaker, J.W., Mehl, M.R., Niederhoffer, K.G.: Psychological aspects of natural language use: our words, our selves. Ann. Rev. Psychol. 54(1), 547–577 (2003)Potthast, M., Stein, B., Barrón-Cedeño, A., Rosso, P.: An evaluation framework for plagiarism detection. In: COLING 2010: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 997–1005 (2010)Potthast, M., Stein, B., Anderka, M.: A wikipedia-based multilingual retrieval model. In: Plachouras, V., Macdonald, C., Ounis, I., White, R.W., Ruthven, I. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 522–530. Springer, Heidelberg (2008)Potthast, M., Stein, B., Eiselt, A., Barrón-Cedeño, A., Rosso, P.:. Overview of the 1st international competition on plagiarism detection. In: Stein, B., Rosso, P., Stamatatos, E., Koppel, M., Agirre, E., (eds.) Proceedings of the SEPLN 2009 Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 2009), pp. 1–9, 2009. CEUR-WS.org (September 2009). http://ceur-ws.org/Vol-502Potthast, M., Barrón-Cedeño, A., Eiselt, A., Stein, B., Rosso, P.: Overview of the 2nd International Competition on Plagiarism Detection. In: Braschler and Harman [8]Potthast, M., Barrón-Cedeño, A., Eiselt, A., Stein, B., Rosso, P.: Overview of the 2nd international competition on plagiarism detection. In: Braschler, M., Harman, D., Pianta, E., (eds.) Working Notes Papers of the CLEF 2010 Evaluation Labs (September 2010) 2010. http://www.clef-initiative.eu/publication/working-notesPotthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-language plagiarism detection. Lang. Resour. Eval. 45(1), 45–62 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: Petras, V., Forner, P., Clough, P., (eds.) Working Notes Papers of the CLEF 2011 Evaluation Labs (September 2011) (2011). http://www.clef-initiative.eu/publication/working-notesPotthast, M., Gollub, T., Hagen, M., Grabegger, J., Kiesel, J., Michel, M., Oberlander, A., Tippmann, M., Barrón-Cedeño, A., Gupta, P., Rosso, P., Stein, B.: Overview of the 4th international competition on plagiarism detection. In: Forner, P., Karlgren, J., Womser-Hacker, C., (eds.) Working Notes Papers of the CLEF 2012 Evaluation Labs (September 2012) (2012). http://www.clef-initiative.eu/publication/working-notesPotthast, M., Hagen, M., Stein, B., Grabegger, J., Michel, M., Tippmann, M., Welsch, C.: Chatnoir: a search engine for the clueweb09 corpus. In: Hersh, B., Callan, J., Maarek, Y., Sanderson, M., (eds.) 35th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2012), p. 1004 (2012)Potthast, M., Gollub, T., Hagen, M., Tippmann, M., Kiesel, J., Rosso, P., Stamatatos, E., Stein, B.: Overview of the 5th international competition on plagiarism detection. In: Forner, et al. [14]Potthast, M., Hagen, M., Beyer, A., Busse, M., Tippmann, M., Rosso, P., Stein, B.: Overview of the 6th International Competition on Plagiarism Detection. In: Cappellato, et al. [9]Pouliquen, B., Steinberger, R., Ignat, C.: Automatic linking of similar texts across languages. In: Proceedings of Recent Advances in Natural Language Processing III, RANLP 2003, pp. 307–316 (2003)Prakash, A., Saha, S.: Experiments on document chunking and query formation for plagiarism source retrieval-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Rangel, F., Rosso, P., Koppel, M., Stamatatos, E., Inches, G.: Overview of the author profiling task at PAN 2013–notebook for PAN at CLEF 2013. In: Forner, et al. [14]Rangel, F., Rosso, P., Chugur, I., Potthast, M., Trenkman, M., Stein, B., Verhoeven, B., Daelemans, W.: Overview of the 2nd author profiling task at PAN 2014–notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Sanchez-Perez, M., Sidorov, G., Gelbukh, A.: A winning approach to text alignment for text reuse detection at PAN 2014-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Schler, J., Koppel, M., Argamon, S., Pennebaker, J.W.: Effects of age and gender on blogging. In: AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp. 199–205. AAAI (2006)Stamatatos, E.: Intrinsic plagiarism detection using character n-gram profiles. In: Stein, B., Rosso, P., Stamatatos, E., Koppel, M., Agirre, E., (eds.) Proceedings of the SEPLN09 Workshop on Uncovering Plagiarism, Authorship, and Social Software Misuse (PAN 2009), pp. 38–46, 2009. CEUR-WS.org, September 2009. http://ceur-ws.org/Vol-502Stein, B., Meyer zu Eissen, S., Potthast, M.: Strategies for retrieving plagiarized documents. In: Clarke, C., Fuhr, N., Kando, N., Kraaij, W., de Vries, A., (eds.) 30th International ACM Conference on Research and Development in Information Retrieval (SIGIR 2007), pp. 825–826. ACM (2007)Stein, B., Potthast, M., Rosso, P., Barrón-Cedeño, A., Stamatatos, E., Koppel, M.: Fourth international workshop on uncovering plagiarism, authorship, and social software misuse. ACM SIGIR Forum 45, 45–48 (2011)Steinberger, R., Pouliquen, B., Widiger, A., Ignat, C., Erjavec, T., Tufis, D., Varga, D.: The jrc-acquis: a multilingual aligned parallel corpus with +20 languages. In: Proceedings of 5th International Conference on language resources and evaluation LREC 2006 (2006)Suchomel, S., Brandejs, M.: Heterogeneous queries for synoptic and phrasal search-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Villena-Román, J., González-Cristóbal, J.C.: DAEDALUS at PAN 2014: Guessing Tweet Author’s Gender and Age-Notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Vossen, P.: Eurowordnet: a multilingual database of autonomous and language-specific wordnets connected via an inter-lingual index. Int. J. Lexicography 17, 161–173 (2004)Wang, H., Lu, Y., Zhai, C.: Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 783–792 (2010)Weren, E.R.D., Moreira, V.P., de Oliveira, J.P.M.:. Exploring information retrieval features for author profiling-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Williams, K., Chen, H.H., Giles, C.: Supervised ranking for plagiarism source retrieval-notebook for PAN at CLEF 2014. In: Cappellato, et al. [9]Yule, G.: The Statistical Study of Literary Vocabulary. Cambridge University press, Cambridge (1944)Zubarev, D., Sochenkov, I.: Using sentence similarity measure for plagiarism source retrieval-notebook for PAN at CLEF 2014. In: Cappellato, L., et al. [9

Plagiarism meets paraphrasing: insights for the new generation in automatic plagiarism detection

Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyse the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource which uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analysed in the light of this annotation. The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarising, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analysed, providing critical insights for the improvement of automatic plagiarism detection systems

Improving the Reproducibility of PAN s Shared Tasks

This paper reports on the PAN 2014 evaluation lab which hosts three shared tasks on plagiarism detection, author identification, and author profiling. To improve the reproducibility of shared tasks in general, and PAN’s tasks in particular, the Webis group developed a new web service called TIRA, which facilitates software submissions. Unlike many other labs, PAN asks participants to submit running softwares instead of their run output. To deal with the organizational overhead involved in handling software submissions, the TIRA experimentation platform helps to significantly reduce the workload for both participants and organizers, whereas the submitted softwares are kept in a running state. This year, we addressed the matter of responsibility of successful execution of submitted softwares in order to put participants back in charge of executing their software at our site. In sum, 57 softwares have been submitted to our lab; together with the 58 software submissions of last year, this forms the largest collection of softwares for our three tasks to date, all of which are readily available for further analysis. The report concludes with a brief summary of each task.This work was partially supported by the WIQ-EI IRSESproject (Grant No. 269180) within the FP7 Marie Curie action.Potthast, M.; Gollub, T.; Rangel, F.; Rosso, P.; Stamatatos, E.; Stein, B. (2014). Improving the Reproducibility of PAN s Shared Tasks. En Information Access Evaluation. Multilinguality, Multimodality, and Interaction: 5th International Conference of the CLEF Initiative, CLEF 2014, Sheffield, UK, September 15-18, 2014. Proceedings. Springer Verlag (Germany). 268-299. https://doi.org/10.1007/978-3-319-11382-1_22S26829

Overview of the 2nd international competition on plagiarism detection

This paper overviews 18 plagiarism detectors that have been developed and evaluated within PAN'10. We start with a unified retrieval process that summarizes the best practices employed this year. Then, the detectors' performances are evaluated in detail, highlighting several important aspects of plagiarism detection, such as obfuscation, intrinsic vs. external plagiarism, and plagiarism case length. Finally, all results are compared to those of last year's competition

Plagiarism Detection in Texts Obfuscated with Homoglyphs

Homoglyphs can be used for disguising plagiarized text by replacing letters in source texts with visually identical letters from other scripts. Most current plagiarism detection systems are not able to detect plagiarism when text has been obfuscated using homoglyphs. In this work, we present two alternative approaches for detecting plagiarism in homoglyph obfuscated texts. The first approach utilizes the Unicode list of confusables to replace homoglyphs with visually identical letters, while the second approach uses a similarity score computed using normalized hamming distance to match homoglyph obfuscated words with source words. Empirical testing on datasets from PAN-2015 shows that both approaches perform equally well for plagiarism detection in homoglyph obfuscated texts

Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection

[EN] Although paraphrasing is the linguistic mechanism underlying many plagiarism cases, little attention has been paid to its analysis in the framework of automatic plagiarism detection. Therefore, state-of-the-art plagiarism detectors find it difficult to detect cases of paraphrase plagiarism. In this article, we analyze the relationship between paraphrasing and plagiarism, paying special attention to which paraphrase phenomena underlie acts of plagiarism and which of them are detected by plagiarism detection systems. With this aim in mind, we created the P4P corpus, a new resource that uses a paraphrase typology to annotate a subset of the PAN-PC-10 corpus for automatic plagiarism detection. The results of the Second International Competition on Plagiarism Detection were analyzed in the light of this annotation.The presented experiments show that (i) more complex paraphrase phenomena and a high density of paraphrase mechanisms make plagiarism detection more difficult, (ii) lexical substitutions are the paraphrase mechanisms used the most when plagiarizing, and (iii) paraphrase mechanisms tend to shorten the plagiarized text. For the first time, the paraphrase mechanisms behind plagiarism have been analyzed, providing critical insights for the improvement of automatic plagiarism detection systems.We would like to thank the people who participated in the annotation of the P4P corpus, Horacio Rodriguez for his helpful advice as experienced researcher, and the reviewers of this contribution for their valuable comments to improve this article. This research work was partially carried out during the tenure of an ERCIM "Alain Bensoussan" Fellowship Programme. The research leading to these results received funding from the EU FP7 Programme 2007-2013 (grant no. 246016), the MICINN projects TEXT-ENTERPRISE 2.0 and TEXT-KNOWLEDGE 2.0 (TIN2009-13391), the EC WIQ-EI IRSES project (grant no. 269180), and the FP7 Marie Curie People Programme. The research work of A. Barron-Cedeno and M. Vila was financed by the CONACyT-Mexico 192021 grant and the MECD-Spain FPU AP2008-02185 grant, respectively. The research work of A. Barron-Cedeno was partially done in the framework of his Ph.D. at the Universitat Politecnica de Valencia.Barrón Cedeño, LA.; Vila, M.; Martí, MA.; Rosso, P. (2013). Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection. Computational Linguistics. 39(4):917-947. https://doi.org/10.1162/COLI_a_00153S917947394Barzilay, Regina. 2003. Information Fusion for Multidocument Summarization: Paraphrasing and Generation. Ph.D. thesis, Columbia University, New York.Barzilay, R., & Lee, L. (2003). Learning to paraphrase. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - NAACL ’03. doi:10.3115/1073445.1073448Barzilay, Regina and Kathleen R. McKeown. 2001. Extracting paraphrases from a parallel corpus. In Proceedings of the 39th Annual Meeting of the Association for Computational Linguistics (ACL 2001), pages 50–57, Toulouse.Barzilay, R., McKeown, K. R., & Elhadad, M. (1999). Information fusion in the context of multi-document summarization. Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics -. doi:10.3115/1034678.1034760Bhagat, Rahul. 2009. Learning Paraphrases from Text. Ph.D. thesis, University of Southern California, Los Angeles.Cheung, Mei Ling Lisa. 2009. Merging Corpus Linguistics and Collaborative Knowledge Construction. Ph.D. thesis, University of Birmingham, Birmingham.Cohn, T., Callison-Burch, C., & Lapata, M. (2008). Constructing Corpora for the Development and Evaluation of Paraphrase Systems. Computational Linguistics, 34(4), 597-614. doi:10.1162/coli.08-003-r1-07-044Dras, Mark. 1999. Tree Adjoining Grammar and the Reluctant Paraphrasing of Text. Ph.D. thesis, Macquarie University, Sydney.Faigley, L., & Witte, S. (1981). Analyzing Revision. College Composition and Communication, 32(4), 400. doi:10.2307/356602Fujita, Atsushi. 2005. Automatic Generation of Syntactically Well-formed and Semantically Appropriate Paraphrases. Ph.D. thesis, Nara Institute of Science and Technology, Nara.Grozea, C., & Popescu, M. (2010). Who’s the Thief? Automatic Detection of the Direction of Plagiarism. Lecture Notes in Computer Science, 700-710. doi:10.1007/978-3-642-12116-6_59GÜLICH, E. (2003). Conversational Techniques Used in Transferring Knowledge between Medical Experts and Non-experts. Discourse Studies, 5(2), 235-263. doi:10.1177/1461445603005002005Harris, Z. S. (1957). Co-Occurrence and Transformation in Linguistic Structure. Language, 33(3), 283. doi:10.2307/411155KETCHEN Jr., D. J., & SHOOK, C. L. (1996). THE APPLICATION OF CLUSTER ANALYSIS IN STRATEGIC MANAGEMENT RESEARCH: AN ANALYSIS AND CRITIQUE. Strategic Management Journal, 17(6), 441-458. doi:10.1002/(sici)1097-0266(199606)17:63.0.co;2-gMcCarthy, D., & Navigli, R. (2009). The English lexical substitution task. Language Resources and Evaluation, 43(2), 139-159. doi:10.1007/s10579-009-9084-1Recasens, M., & Vila, M. (2010). On Paraphrase and Coreference. Computational Linguistics, 36(4), 639-647. doi:10.1162/coli_a_00014Shimohata, Mitsuo. 2004. Acquiring Paraphrases from Corpora and Its Application to Machine Translation. Ph.D. thesis, Nara Institute of Science and Technology, Nara.Stein, B., Potthast, M., Rosso, P., Barrón-Cedeño, A., Stamatatos, E., & Koppel, M. (2011). Fourth international workshop on uncovering plagiarism, authorship, and social software misuse. ACM SIGIR Forum, 45(1), 45. doi:10.1145/1988852.198886

On the use of word embedding for cross language plagiarism detection

[EN] Cross language plagiarism is the unacknowledged reuse of text across language pairs. It occurs if a passage of text is translated from source language to target language and no proper citation is provided. Although various methods have been developed for detection of cross language plagiarism, less attention has been paid to measure and compare their performance, especially when tackling with different types of paraphrasing through translation. In this paper, we investigate various approaches to cross language plagiarism detection. Moreover, we present a novel approach to cross language plagiarism detection using word embedding methods and explore its performance against other state-of-the-art plagiarism detection algorithms. In order to evaluate the methods, we have constructed an English-Persian bilingual plagiarism detection corpus (referred to as HAMTA-CL) comprised of seven types of obfuscation. The results show that the word embedding approach outperforms the other approaches with respect to recall when encountering heavily paraphrased passages. On the other hand, translation based approach performs well when the precision is the main consideration of the cross language plagiarism detection system.Asghari, H.; Fatemi, O.; Mohtaj, S.; Faili, H.; Rosso, P. (2019). On the use of word embedding for cross language plagiarism detection. Intelligent Data Analysis. 23(3):661-680. https://doi.org/10.3233/IDA-183985S661680233H. Asghari, K. Khoshnava, O. Fatemi and H. Faili, Developing bilingual plagiarism detection corpus using sentence aligned parallel corpus: Notebook for {PAN} at {CLEF} 2015, In L. Cappellato, N. Ferro, G.J.F. Jones and E. SanJuan, editors, Working Notes of {CLEF} 2015 – Conference and Labs of the Evaluation forum, Toulouse, France, September 8–11, 2015, volume 1391 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2015.A. Barrón-Cede no, M. Potthast, P. Rosso and B. Stein, Corpus and evaluation measures for automatic plagiarism detection, In N. Calzolari, K. Choukri, B. Maegaard, J. Mariani, J. Odijk, S. Piperidis, M. Rosner and D. Tapias, editors, Proceedings of the International Conference on Language Resources and Evaluation, {LREC} 2010, 17–23 May 2010, Valletta, Malta. European Language Resources Association, 2010.A. Barrón-Cede no, P. Rosso, D. Pinto and A. Juan, On cross-lingual plagiarism analysis using a statistical model, In B. Stein, E. Stamatatos and M. Koppel, editors, Proceedings of the ECAI’08 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, Patras, Greece, July 22, 2008, volume 377 of {CEUR} Workshop Proceedings. CEUR-WS.org, 2008.Farghaly, A., & Shaalan, K. (2009). Arabic Natural Language Processing. ACM Transactions on Asian Language Information Processing, 8(4), 1-22. doi:10.1145/1644879.1644881J. Ferrero, F. Agnès, L. Besacier and D. Schwab, A multilingual, multi-style and multi-granularity dataset for cross-language textual similarity detection, In N. Calzolari, K. Choukri, T. Declerck, S. Goggi, M. Grobelnik, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk and S. Piperidis, editors, Proceedings of the Tenth International Conference on Language Resources and Evaluation {LREC} 2016, Portorož, Slovenia, May 23–28, 2016, European Language Resources Association {(ELRA)}, 2016.Franco-Salvador, M., Gupta, P., Rosso, P., & Banchs, R. E. (2016). Cross-language plagiarism detection over continuous-space- and knowledge graph-based representations of language. Knowledge-Based Systems, 111, 87-99. doi:10.1016/j.knosys.2016.08.004Franco-Salvador, M., Rosso, P., & Montes-y-Gómez, M. (2016). A systematic study of knowledge graph analysis for cross-language plagiarism detection. Information Processing & Management, 52(4), 550-570. doi:10.1016/j.ipm.2015.12.004C.K. Kent and N. Salim, Web based cross language plagiarism detection, CoRR, abs/0912.3, 2009.McNamee, P., & Mayfield, J. (2004). Character N-Gram Tokenization for European Language Text Retrieval. Information Retrieval, 7(1/2), 73-97. doi:10.1023/b:inrt.0000009441.78971.beT. Mikolov, K. Chen, G. Corrado and J. Dean, Efficient estimation of word representations in vector space, CoRR, abs/1301.3, 2013.S. Mohtaj, B. Roshanfekr, A. Zafarian and H. Asghari, Parsivar: A language processing toolkit for persian, In N. Calzolari, K. Choukri, C. Cieri, T. Declerck, S. Goggi, K. Hasida, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, A. Moreno, J. Odijk, S. Piperidis and T. Tokunaga, editors, Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan, May 7–12, 2018, European Language Resources Association ELRA, 2018.R.M.A. Nawab, M. Stevenson and P.D. Clough, University of Sheffield – Lab Report for {PAN} at {CLEF} 2010, In M. Braschler, D. Harman and E. Pianta, editors, {CLEF} 2010 LABs and Workshops, Notebook Papers, 22–23 September 2010, Padua, Italy, volume 1176 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2010.G. Oberreuter, G. L’Huillier, S.A. Rios and J.D. Velásquez, Approaches for intrinsic and external plagiarism detection – Notebook for {PAN} at {CLEF} 2011, In V. Petras, P. Forner and P.D. Clough, editors, {CLEF} 2011 Labs and Workshop, Notebook Papers, 19–22 September 2011, Amsterdam, The Netherlands, volume 1177 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2011.Pinto, D., Civera, J., Barrón-Cedeño, A., Juan, A., & Rosso, P. (2009). A statistical approach to crosslingual natural language tasks. Journal of Algorithms, 64(1), 51-60. doi:10.1016/j.jalgor.2009.02.005M. Potthast, A. Barrón-Cede no, A. Eiselt, B. Stein and P. Rosso, Overview of the 2nd international competition on plagiarism detection, In M. Braschler, D. Harman and E. Pianta, editors, {CLEF} 2010 LABs and Workshops, Notebook Papers, 22–23 September 2010, Padua, Italy, volume 1176 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2010.Potthast, M., Barrón-Cedeño, A., Stein, B., & Rosso, P. (2010). Cross-language plagiarism detection. Language Resources and Evaluation, 45(1), 45-62. doi:10.1007/s10579-009-9114-zM. Potthast, A. Eiselt, A. Barrón-Cede no, B. Stein and P. Rosso, Overview of the 3rd international competition on plagiarism detection, In V. Petras, P. Forner and P.D. Clough, editors, {CLEF} 2011 Labs and Workshop, Notebook Papers, 19–22 September 2011, Amsterdam, The Netherlands, volume 1177 of {CEUR} Workshop Proceedings. CEUR-WS.org, 2011.M. Potthast, S. Goering, P. Rosso and B. Stein, Towards data submissions for shared tasks: First experiences for the task of text alignment, In L. Cappellato, N. Ferro, G.J.F. Jones and E. SanJuan, editors, Working Notes of {CLEF} 2015 – Conference and Labs of the Evaluation forum, Toulouse, France, September 8–11, 2015, volume 1391 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2015.Potthast, M., Stein, B., & Anderka, M. (s. f.). A Wikipedia-Based Multilingual Retrieval Model. Advances in Information Retrieval, 522-530. doi:10.1007/978-3-540-78646-7_51B. Pouliquen, R. Steinberger and C. Ignat, Automatic identification of document translations in large multilingual document collections, CoRR, abs/cs/060, 2006.B. Stein, E. Stamatatos and M. Koppel, Proceedings of the ECAI’08 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, Patras, Greece, July 22, 2008, volume 377 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2008.J. Wieting, M. Bansal, K. Gimpel and K. Livescu, Towards universal paraphrastic sentence embeddings, CoRR, abs/1511.0, 2015.V. Zarrabi, J. Rafiei, K. Khoshnava, H. Asghari and S. Mohtaj, Evaluation of text reuse corpora for text alignment task of plagiarism detection, In L. Cappellato, N. Ferro, G.J.F. Jones and E. SanJuan, editors, Working Notes of {CLEF} 2015 – Conference and Labs of the Evaluation forum, Toulouse, France, September 8–11, 2015, volume 1391 of {CEUR} Workshop Proceedings, CEUR-WS.org, 2015.Barrón-Cedeño, A., Gupta, P., & Rosso, P. (2013). Methods for cross-language plagiarism detection. Knowledge-Based Systems, 50, 211-217. doi:10.1016/j.knosys.2013.06.01

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