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[EN] Several methods have been proposed for determining plagiarism between pairs of sentences, passages or even full documents. However, the majority of these methods fail to reliably detect paraphrase plagiarism due to the high complexity of the task, even for human beings. Paraphrase plagiarism identi cation consists in automatically recognizing document fragments that contain re-used text, which is intentionally hidden by means of some rewording practices such as semantic equivalences, discursive changes, and morphological or lexical substitutions. Our main hypothesis establishes that the original author's writing style ngerprint prevails in the plagiarized text even when paraphrases occur. Thus, in this paper we propose a novel text representation scheme that gathers both content and style characteristics of texts, represented by means of character-level features. As an additional contribution, we describe the methodology followed for the construction of an appropriate corpus for the task of paraphrase plagiarism identi cation, which represents a new valuable resource to the NLP community for future research work in this field.

[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.

An important task in plagiarism detection is determining and measuring similar text portions between a given pair of documents. One of the main difficulties of this task resides on the fact that reused text is commonly modified with the aim of covering or camouflaging the plagiarism. Another difficulty is that not all similar text fragments are examples of plagiarism, since thematic coincidences also tend to produce portions of similar text. In order to tackle these problems, we propose a novel method for detecting likely portions of reused text. This method is able to detect common actions performed by plagiarists such as word deletion, insertion and transposition, allowing to obtain plausible portions of reused text. We also propose representing the identified reused text by means of a set of features that denote its degree of plagiarism, relevance and fragmentation. This new representation aims to facilitate the recognition of plagiarism by considering diverse characteristics of the reused text during the classification phase. Experimental results employing a supervised classification strategy showed that the proposed method is able to outperform traditionally used approaches. 2012 Elsevier Ltd. All rights reserved.