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

Cross-language plagiarism detection using multilingual semantic network

The final publication is available at Springer via http://10.1007/978-3-642-36973-5_66Cross-language plagiarism refers to the type of plagiarism where the source and suspicious documents are in different languages. Plagiarism detection across languages is still in its infancy state. In this article, we propose a new graph-based approach that uses a multilingual semantic network to compare document paragraphs in different languages. In order to investigate the proposed approach, we used the German-English and Spanish-English cross-language plagiarism cases of the PAN-PC¿11 corpus. We compare the obtained results with two state-of-the-art models. Experimental results indicate that our graph-based approach is a good alternative for cross-language plagiarism detectionWe thank the Conselleria d′educació, Formació i Ocupació of the Generalitat Valenciana for funding the work of the first author with the Gerónimo Forteza program. The research has been carried out in the framework of the European Commission WIQ-EI IRSES project (no. 269180) and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Franco Salvador, M.; Gupta, PA.; Rosso ., P. (2013). Cross-language plagiarism detection using multilingual semantic network. En Advances in Information Retrieval. Springer Verlag (Germany). 7814:710-713. https://doi.org/10.1007/978-3-642-36973-5_66S7107137814Barrón-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)Havasi, C.: Conceptnet 3: A flexible, multilingual semantic network for common sense knowledge. In: The 22nd Conference on Artificial Intelligence (2007)Mcnamee, P., Mayfield, J.: Character n-gram tokenization for European language text retrieval. Inf. Retr. 7(1-2), 73–97 (2004)Montes-y-Gómez, M., Gelbukh, A., López-López, A., Baeza-Yates, R.: Flexible Comparison of Conceptual GraphsWork done under partial support of CONACyT, CGEPI-IPN, and SNI, Mexico. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 102–111. Springer, Heidelberg (2001)Navigli, R., Ponzetto, S.P.: Babelnet: building a very large multilingual semantic network. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, ACL 2010, Stroudsburg, PA, USA, pp. 216–225 (2010)Potthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-language plagiarism detection. Language Resources and Evaluation, Special Issue on Plagiarism and Authorship Analysis 45(1) (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd international competition on plagiarism detection. In: CLEF (Notebook Papers/Labs/Workshop) (2011

A Multilingual, Multi-Style and Multi-Granularity Dataset for Cross-Language Textual Similarity Detection

International audienceIn this paper we describe our effort to create a dataset for the evaluation of cross-language textual similarity detection. We present pre-existing corpora and their limits and we explain the various gathered resources to overcome these limits and build our enriched dataset. The proposed dataset is multilingual, includes cross-language alignment for different granularities (from chunk to document), is based on both parallel and comparable corpora and contains human and machine translated texts. Moreover, it includes texts written by multiple types of authors (from average to professionals). With the obtained dataset, we conduct a systematic and rigorous evaluation of several state-of-the-art cross-language textual similarity detection methods. The evaluation results are reviewed and discussed. Finally, dataset and scripts are made publicly available on GitHub: http://github.com/FerreroJeremy/Cross-Language-Dataset

English-Persian Plagiarism Detection based on a Semantic Approach

Plagiarism which is defined as “the wrongful appropriation of other writers’ or authors’ works and ideas without citing or informing them” poses a major challenge to knowledge spread publication. Plagiarism has been placed in four categories of direct, paraphrasing (rewriting), translation, and combinatory. This paper addresses translational plagiarism which is sometimes referred to as cross-lingual plagiarism. In cross-lingual translation, writers meld a translation with their own words and ideas. Based on monolingual plagiarism detection methods, this paper ultimately intends to find a way to detect cross-lingual plagiarism. A framework called Multi-Lingual Plagiarism Detection (MLPD) has been presented for cross-lingual plagiarism analysis with ultimate objective of detection of plagiarism cases. English is the reference language and Persian materials are back translated using translation tools. The data for assessment of MLPD were obtained from English-Persian Mizan parallel corpus. Apache’s Solr was also applied to record the creep of the documents and their indexation. The accuracy mean of the proposed method revealed to be 98.82% when employing highly accurate translation tools which indicate the high accuracy of the proposed method. Also, Google translation service showed the accuracy mean to be 56.9%. These tests demonstrate that improved translation tools enhance the accuracy of the proposed method

La escritura académica e Internet: ciberplagio entre los estudiantes universitarios

This research aims to present a number of findings on the perception that university students have on academic plagiarism. Data has been collected on the procedures of copy and paste, paraphrase, translation, as well as the need for citation of resources taken from the Internet. This study was carried out at the University of Lleida (UdL), via an online questionnaire administered to 1150 first-year students. The results show similar figures in the understanding of plagiarism with respect to two procedures: copy and paste (69.3%) and paraphrase (68.3%). In the case of translation, the figure is higher with 82.1% of students considering that translating a text is plagiarism. Regarding the need to cite digital sources, 13.6% argue it is not necessary. When analysing the results according to the different faculties and affiliated schools, no pattern of behaviour has been detected in relation to the typology of the degree students were enrolled in, but a trend towards different behaviours can be observed in the two faculties in which students have received specific training within the framework of subjects of their degrees (Faculty of Nursing and Physiotherapy and Faculty of Education, Psychology and Social Work). In these cases, the figures related to acknowledging plagiarism procedures are higher and so is the percentage of students who say that resources taken from the Internet should be cited. This leads us to conclude, in line with other studies that have dealt with the same subject, that training is key to tackling the issue of plagiarism in higher education.Esta investigación pretende presentar una serie de hallazgos sobre la percepción que los estudiantes universitarios tienen del plagio académico. Se han recopilado datos sobre los procedimientos de copiar y pegar, parafrasear y traducir, así como sobre la necesidad de citar los recursos tomados de Internet. Este estudio se realizó en la Universidad de Lleida (UdL), a través de un cuestionario online que se administró a 1.150 alumnos de primer curso. Los resultados muestran cifras similares en la comprensión del plagio con respecto a dos procedimientos: copiar y pegar (69,3%) y parafrasear (68,3%). En el caso de la traducción, la cifra es mayor con el 82,1% de los estudiantes que consideran que traducir un texto es plagio. En cuanto a la necesidad de mencionar las fuentes digitales, el 13,6% de los estudiantes argumenta que no es preciso citarlas. Al analizar los resultados en función de las distintas facultades y escuelas asociadas, no se ha detectado ningún patrón de comportamiento en relación con la tipología de las titulaciones en las que se matricularon los estudiantes, aunque se observa una tendencia hacia comportamientos diferentes en las dos facultades en las que han recibido formación específica en el marco de las asignaturas de sus titulaciones (Facultad de Enfermería y Fisioterapia y Facultad de Educación, Psicología y Trabajo Social). En estos casos, las cifras relacionadas con el reconocimiento de los procedimientos de plagio son mayores y también lo es el porcentaje de estudiantes que dice que se debe citar los recursos tomados de Internet. Ello nos lleva a concluir, en línea con otros estudios que han tratado el mismo tema, que la formación es clave para abordar la cuestión del plagio en la Educación Superior

Approach to a semantic and pragmatic analysis of indicators for the determination of authorship in forensic Linguistic

[EN] The purpose of this work is to analyze, taking as a reference a small sample of short texts, some indicators of semanticand pragmatic nature that we consider have not been treated in the literature on authorship attribution in forensic Linguistics. Both manual stylistic analyses, which have focused mainly on aspects such as punctuation, spelling, typos and combinations of certain word classes, as well as stylometric analyses, basically provide quantitative and statistical information. We consider, however, that a study to determine authorship -especially in the case of the selected texts- should be based on a manual stylisticanalysis that takes into account the presence in the corresponding texts of certain speech acts, the use of different sentence modalities, the use of different connectors and, above all, the semantism of combinations and lexical units with an evaluative nature.[ES] El objetivo de este trabajo es el de analizar, tomando como referencia una pequeña muestra de textos cortos, algunos indicadores de carácter semántico y pragmático que consideramos que no han sido tratados en la bibliografía sobre determinación de autoría en Lingüística forense. Tanto los análisis estilísticos manuales, que se han centrado fundamentalmente en aspectos como la puntuación, la ortografía, las erratas y las combinaciones de ciertas categorías gramaticales, como los análisis estilométricos, proporcionan básicamente una información cuantitativa y estadística. Consideramos, en cambio, que un estudio de determinación de autoría -sobre todo en el caso de los textos seleccionados- debe tener como base un análisisestilístico manual que tenga en cuenta la presencia en los textos correspondientes de ciertos actos de habla, el uso de distintas modalidades oracionales, la aparición de diferentes conectores y, sobre todo, el semantismo de combinaciones y unidades léxicas con carácter valorativo.Muñoz Núñez, MD. (2022). Aproximación a un análisis semántico y pragmático de indicadores para la determinación de autoría en Lingüística forense. Revista de Lingüística y Lenguas Aplicadas. 17:99-113. https://doi.org/10.4995/rlyla.2022.16064991131

Detección intrínseca de plagio

[EN] In this thesis we focus on the intrinsic plagiarism detection by means of the application of diverse stylistic traditional measures and of two new measures proposed that we are called Index of Local Concorcande or ICL and, his generalization, General Index of Local Concordance or IGCL. In order to do realize the detection we propose two methods that use a set of stylistic measures. The first method is learn of the set example of documents the relevant characteristics, to be able to separate in plagiarism documents and no plagiarism documents. The second one, allows to recognize the documents fragments that not preserve the style of the majority of the document and therefore might be considered as plagiarism fragments.[ES] En esta tesis nos centramos en realizar la detección intrínseca de plagio mediante la aplicación de diversas medidas estilísticas tradicionales y de dos nuevas medidas propuestas a las que llamamos Índice de Concordancia Local o ICL y, su generalización, Índice General de Concordancia Local o IGCL. Para realizar la detección proponemos dos métodos que emplean el conjunto de medidas estilísticas. El primer método, es capaz de aprender de un conjunto de documentos ejemplo las características necesarias, para poder realizar la separación de los documentos con plagio de aquellos que se encuentran libres de plagio. El segundo, permite reconocer los fragmentos de los documentos que no conservan el estilo de la mayoría del documento y que por tanto podrían considerarse como fragmentos sospechosos de plagio.Pérez Afonso, J. (2013). Detección intrínseca de plagio. http://hdl.handle.net/10251/43831Archivo delegad

Paraphrase type identification for plagiarism detection using contexts and word embeddings

Paraphrase types have been proposed by researchers as the paraphrasing mechanisms underlying acts of plagiarism. Synonymous substitution, word reordering and insertion/deletion have been identified as some of the common paraphrasing strategies used by plagiarists. However, similarity reports generated by most plagiarism detection systems provide a similarity score and produce matching sections of text with their possible sources. In this research we propose methods to identify two important paraphrase types – synonymous substitution and word reordering in paraphrased, plagiarised sentence pairs. We propose a three staged approach that uses context matching and pretrained word embeddings for identifying synonymous substitution and word reordering. Our proposed approach indicates that the use of Smith Waterman Algorithm for Plagiarism Detection and ConceptNet Numberbatch pretrained word embeddings produces the best performance in terms of F1 scores. This research can be used to complement similarity reports generated by currently available plagiarism detection systems by incorporating methods to identify paraphrase types for plagiarism detection

Knowledge Graphs as Context Models: Improving the Detection of Cross-Language Plagiarism with Paraphrasing

Cross-language plagiarism detection attempts to identify and extract automatically plagiarism among documents in different languages. Plagiarized fragments can be translated verbatim copies or may alter their structure to hide the copying, which is known as paraphrasing and is more difficult to detect. In order to improve the paraphrasing detection, we use a knowledge graph-based approach to obtain and compare context models of document fragments in different languages. Experimental results in German-English and Spanish-English cross-language plagiarism detection indicate that our knowledge graph-based approach offers a better performance compared to other state-of-the-art models.The research has been carried out in the framework of the European Commission WIQ-EIIRSES (no. 269180) and DIANA-APPLICATIONS - Finding Hidden Knowledge in Texts:Applications (TIN2012-38603-C02-01) projects as well as the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.Franco-Salvador, M.; Gupta, P.; Rosso, P. (2013). Knowledge Graphs as Context Models: Improving the Detection of Cross-Language Plagiarism with Paraphrasing. En Bridging Between Information Retrieval and Databases: PROMISE Winter School 2013, Bressanone, Italy, February 4-8, 2013. Revised Tutorial Lectures. Springer Verlag (Germany). 227-236. https://doi.org/10.1007/978-3-642-54798-0_12S227236Barrón-Cedeño, A., Vila, M., Martí, M., Rosso, P.: Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection. Computational Linguistics 39(4) (2013)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: Proc. of the ECAI 2008 Workshop on Uncovering Plagiarism, Authorship and Social Software Misuse, PAN 2008 (2008)Franco-Salvador, M., Gupta, P., Rosso, P.: Cross-language plagiarism detection using BabelNet’s statistical dictionary. Computación y Sistemas, Revista Iberoamericana de Computación 16(4), 383–390 (2012)Franco-Salvador, M., Gupta, P., Rosso, P.: Cross-language plagiarism detection using a multilingual semantic network. In: Serdyukov, P., Braslavski, P., Kuznetsov, S.O., Kamps, J., Rüger, S., Agichtein, E., Segalovich, I., Yilmaz, E. (eds.) ECIR 2013. LNCS, vol. 7814, pp. 710–713. Springer, Heidelberg (2013)Franco-Salvador, M., Gupta, P., Rosso, P.: Graph-based similarity analysis: a new approach to cross-language plagiarism detection. Journal of the Spanish Society of Natural Language Processing (Sociedad Espaola de Procesamiento del Languaje Natural) (50) (2013)Montes-y-Gómez, M., Gelbukh, A., López-López, A., Baeza-Yates, R.: Flexible comparison of conceptual graphs. In: Mayr, H.C., Lazanský, J., Quirchmayr, G., Vogel, P. (eds.) DEXA 2001. LNCS, vol. 2113, pp. 102–111. Springer, Heidelberg (2001)Gupta, P., Barrón-Cedeño, A., Rosso, P.: Cross-language high similarity search using a conceptual thesaurus. In: Catarci, T., Forner, P., Hiemstra, D., Peñas, A., Santucci, G. (eds.) CLEF 2012. LNCS, vol. 7488, pp. 67–75. Springer, Heidelberg (2012)Mcnamee, P., Mayfield, J.: Character n-gram tokenization for European language text retrieval. Information Retrieval 7(1), 73–97 (2004)Miller, G.A., Leacock, C., Tengi, R., Bunker, R.T.: A semantic concordance. In: Proceedings of the Workshop on Human Language Technology, HLT 1993, pp. 303–308. Association for Computational Linguistics, Stroudsburg (1993)Navigli, R., Ponzetto, S.P.: BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network. Artificial Intelligence 193, 217–250 (2012)Och, F.J., Ney, H.: A systematic comparison of various statistical alignment models. Computational Linguistics 29(1), 19–51 (2003)Potthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: An evaluation framework for plagiarism detection. In: Proc. of the 23rd Int. Conf. on Computational Linguistics, COLING 2010, Beijing, China, pp. 997–1005 (2010)Potthast, M., Barrón-Cedeño, A., Stein, B., Rosso, P.: Cross-language plagiarism detection. Language Resources and Evaluation, Special Issue on Plagiarism and Authorship Analysis 45(1), 45–62 (2011)Potthast, M., Eiselt, A., Barrón-Cedeño, A., Stein, B., Rosso, P.: Overview of the 3rd int. competition on plagiarism detection. In: CLEF (Notebook Papers/Labs/Workshop) (2011)Potthast, M., Gollub, T., Hagen, M., Kiesel, J., Michel, M., Oberländer, A., Tippmann, M., Barrón-Cedeño, A., Gupta, P., Rosso, P., et al.: Overview of the 4th international competition on plagiarism detection. In: CLEF (Online Working Notes/Labs/Workshop) (2012)Pouliquen, B., Steinberger, R., Ignat, C.: Automatic linking of similar texts across languages. In: Proc. Recent Advances in Natural Language Processing III, RANLP 2003, pp. 307–316 (2003)Schmid, H.: Probabilistic part-of-speech tagging using decision trees. In: Proc. Int. Conf. on New Methods in Language Processing (1994)Stein, B., zu Eissen, S.M., Potthast, M.: Strategies for retrieving plagiarized documents. In: Proc. of the 30th Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 825–826. ACM (2007)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: Proc. 5th Int. Conf. on Language Resources and Evaluation, LREC 2006 (2006)Vossen, P.: Eurowordnet: A multilingual database of autonomous and language-specific wordnets connected via an inter-lingual index. Proc. Int. Journal of Lexicography 17 (2004

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). 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