Investigating features and techniques for Arabic authoriship attribution

Authorship attribution is the problem of identifying the true author of a disputed text. Throughout history, there have been many examples of this problem concerned with revealing genuine authors of works of literature that were published anonymously, and in some cases where more than one author claimed authorship of the disputed text. There has been considerable research effort into trying to solve this problem. Initially these efforts were based on statistical patterns, and more recently they have centred on a range of techniques from artificial intelligence. An important early breakthrough was achieved by Mosteller and Wallace in 1964 [15], who pioneered the use of ‘function words’ – typically pronouns, conjunctions and prepositions – as the features on which to base the discovery of patterns of usage relevant to specific authors. The authorship attribution problem has been tackled in many languages, but predominantly in the English language. In this thesis the problem is addressed for the first time in the Arabic Language. We therefore investigate whether the concept of functions words in English can also be used in the same way for authorship attribution in Arabic. We also describe and evaluate a hybrid of evolutionary algorithms and linear discriminant analysis as an approach to learn a model that classifies the author of a text, based on features derived from Arabic function words. The main target of the hybrid algorithm is to find a subset of features that can robustly and accurately classify disputed texts in unseen data. The hybrid algorithm also aims to do this with relatively small subsets of features. A specialised dataset was produced for this work, based on a collection of 14 Arabic books of different natures, representing a collection of six authors. This dataset was processed into training and test partitions in a way that provides a diverse collection of challenges for any authorship attribution approach. The combination of the successful list of Arabic function words and the hybrid algorithm for classification led to satisfying levels of accuracy in determining the author of portions of the texts in test data. The work described here is the first (to our knowledge) that investigates authorship attribution in the Arabic knowledge using computational methods. Among its contributions are: the first set of Arabic function words, the first specialised dataset aimed at testing Arabic authorship attribution methods, a new hybrid algorithm for classifying authors based on patterns derived from these function words, and, finally, a number of ideas and variants regarding how to use function words in association with character level features, leading in some cases to more accurate results

The lexico-phraseology of THE and A/AN in spoken English: a corpus-based study

The English articles (THE, A, AN) are normally described in terms of the grammar of the language. This is only natural, since they are extremely frequent, fit into certain well-defined syntactic slots, and usually help to communicate only very broad aspects of textual meaning. However, as John Sinclair has pointed out (1999, pp.160-161), the articles are also found as components of many lexico-phraseological units, and in such cases a normal grammatical description may not be of relevance. An example he gives is the presence of A in the phrase 'come to a head', where ‘A has little more status than that of a letter of the alphabet’ (p.161). Sinclair also makes the observation that, ‘I do not know of an estimate of the proportion of instances of A, for example, that are not a realisation of the choice of article but of the realisation of part of a multi-word expression.’ (p.161). The present paper addresses the questions raised by Sinclair, and does so with reference to both the definite and the indefinite article. It focuses, in particular, on the spoken language, and presents the results of analyses of random samples of the articles in the spoken component of the British National Corpus (hereafter BNC-spkn). According to the data in Leech et al (2001, p.144), THE is the most frequent word in BNC-spkn and A is the sixth most frequent (a rank position which remains unaltered when the frequencies of A and AN are combined). Using the BNCweb interface, and specifying that the relevant word forms should be ‘articles’, the total numbers of tokens are: an 19,049; a 200,004; the 409,060. Since the numbers are very high, the samples investigated also contained a reasonably large number of tokens (500). The relative samples corresponded to the following proportions of tokens in BNC-spkn: an 2.62%, a 0.25%, the 0.12%. The latter two are very low percentages, and for this reason, three separate samples of each were investigated, in order to see the extent to which the samples differed. Analysis of article usage was carried out in the first instance by reading right-sorted concordance lines. Whenever doubts arose, larger contexts were retrieved from the corpus. Various reference works were also consulted, including Berry (1993), Francis et al (1998), and various corpus-based dictionaries and grammars. The data presented includes: description of the various types of lexico-phraseological unit found; the proportions of the samples judged to involve the different lexico-phraseological phenomena identified; the problems encountered when deciding whether or not phraseology is an important factor in specific instances of article usage; and the number of tokens in each sample which were in some way irrelevant, for example because they involved speaker repetition of the article, or the non-completion of a noun phrase

Proceedings of the Fifth Workshop on NLP for Similar Languages, Varieties and Dialects (VarDial 2018)

Peer reviewe

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

On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism

Barrón Cedeño, LA. (2012). On the Mono- and Cross-Language Detection of Text Re-Use and Plagiarism [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/16012Palanci

The similarity metric

A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new ``normalized information distance'', based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the {\em similarity metric}. This theory forms the foundation for a new practical tool. To evidence generality and robustness we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.Comment: 13 pages, LaTex, 5 figures, Part of this work appeared in Proc. 14th ACM-SIAM Symp. Discrete Algorithms, 2003. This is the final, corrected, version to appear in IEEE Trans Inform. T

CLARIN

The book provides a comprehensive overview of the Common Language Resources and Technology Infrastructure – CLARIN – for the humanities. It covers a broad range of CLARIN language resources and services, its underlying technological infrastructure, the achievements of national consortia, and challenges that CLARIN will tackle in the future. The book is published 10 years after establishing CLARIN as an Europ. Research Infrastructure Consortium

CLARIN. The infrastructure for language resources

CLARIN, the "Common Language Resources and Technology Infrastructure", has established itself as a major player in the field of research infrastructures for the humanities. This volume provides a comprehensive overview of the organization, its members, its goals and its functioning, as well as of the tools and resources hosted by the infrastructure. The many contributors representing various fields, from computer science to law to psychology, analyse a wide range of topics, such as the technology behind the CLARIN infrastructure, the use of CLARIN resources in diverse research projects, the achievements of selected national CLARIN consortia, and the challenges that CLARIN has faced and will face in the future. The book will be published in 2022, 10 years after the establishment of CLARIN as a European Research Infrastructure Consortium by the European Commission (Decision 2012/136/EU)