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. 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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. 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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. 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Automatic Categorization for Improving Spanish into Spanish Sign Language Machine Translation

This paper describes a preprocessing module for improving the performance of a Spanish into Spanish Sign Language (Lengua de Signos Espanola: LSE) translation system when dealing with sparse training data. This preprocessing module replaces Spanish words with associated tags. The list with Spanish words (vocabulary) and associated tags used by this module is computed automatically considering those signs that show the highest probability of being the translation of every Spanish word. This automatic tag extraction has been compared to a manual strategy achieving almost the same improvement. In this analysis, several alternatives for dealing with non-relevant words have been studied. Non-relevant words are Spanish words not assigned to any sign. The preprocessing module has been incorporated into two well-known statistical translation architectures: a phrase-based system and a Statistical Finite State Transducer (SFST). This system has been developed for a specific application domain: the renewal of Identity Documents and Driver's License. In order to evaluate the system a parallel corpus made up of 4080 Spanish sentences and their LSE translation has been used. The evaluation results revealed a significant performance improvement when including this preprocessing module. In the phrase-based system, the proposed module has given rise to an increase in BLEU (Bilingual Evaluation Understudy) from 73.8% to 81.0% and an increase in the human evaluation score from 0.64 to 0.83. In the case of SFST, BLEU increased from 70.6% to 78.4% and the human evaluation score from 0.65 to 0.82

Crowdsourcing for Language Resource Development: Criticisms About Amazon Mechanical Turk Overpowering Use

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This is the author’s version of a work that was accepted for publication in Journal of Visual Languages and Computing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Visual Languages and Computing,23, 3, (2012) DOI: 10.1016/j.jvlc.2012.01.003This work presents a new approach to the synthesis of Spanish Sign Language (LSE). Its main contributions are the use of a centralized relational database for storing sign descriptions, the proposal of a new input notation and a new avatar design, the skeleton structure of which improves the synthesis process. The relational database facilitates a highly detailed phonologic description of the signs that include parameter synchronization and timing. The centralized database approach has been introduced to allow the representation of each sign to be validated by the LSE National Institution, FCNSE. The input notation, designated HLSML, presents multiple levels of abstraction compared with current input notations. Redesigned input notation is used to simplify the description and the manual definition of LSE messages. Synthetic messages obtained using our approach have been evaluated by deaf users; in this evaluation a maximum recognition rate of 98.5% was obtained for isolated signs and a recognition rate of 95% was achieved for signed sentences

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Morphology is a key component for many Language Technology applications. However, morphological relations, especially those relying on the derivation and compounding processes, are often addressed in a superficial manner. In this article, we focus on assessing the relevance of deep and motivated morphological knowledge in Natural Language Processing applications. We first describe an annotation experiment whose goal is to evaluate the role of morphology for one task, namely Question Answering (QA). We then highlight the kind of linguistic knowledge that is necessary for this particular task and propose a qualitative analysis of morphological phenomena in order to identify the morphological processes that are most relevant. Based on this study, we perform an intrinsic evaluation of existing tools and resources for French morphology, in order to quantify their coverage. Our conclusions provide helpful insights for using and building appropriate morphological resources and tools that could have a significant impact on the application performance

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