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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. This work is the result of the collaboration in the framework of the CONACYT Thematic Networks program (RedTTL Language Technologies Network) and the WIQ-EI IRSES project (Grant No. 269180) within the FP7 Marie Curie action. The first author was supported by CONACYT (Scholarship 258345/224483). The second, third, and sixth authors were partially supported by CONACyT (Project Grants 258588 and 2410). The work of the fourth author was partially supported by the SomEMBED TIN2015-71147-C2-1-P MINECO research project and by the Generalitat Valenciana under the Grant ALMAMATER (PrometeoII/2014/030).

We apply the integrated syntactic graph feature extraction methodology to the task of automatic authorship detection. This graph-based representation allows integrating different levels of language description into a single structure. We extract textual patterns based on features obtained from shortest path walks over integrated syntactic graphs and apply them to determine the authors of documents. On average, our method outperforms the state of the art approaches and gives consistently high results across different corpora, unlike existing methods. Our results show that our textual patterns are useful for the task of authorship attribution.

[EN] We present a comprehensive study on the use of autoencoders for modelling text data, in which (differently from previous studies) we focus our attention on the various issues. We explore the suitability of two different models binary deep autencoders (bDA) and replicated-softmax deep autencoders (rsDA) for constructing deep autoencoders for text data at the sentence level. We propose and evaluate two novel metrics for better assessing the text-reconstruction capabilities of autoencoders. We propose an automatic method to find the critical bottleneck dimensionality for text representations (below which structural information is lost); and finally we conduct a comparative evaluation across different languages, exploring the regions of critical bottleneck dimensionality and its relationship to language perplexity. & 2015 Elsevier B.V. All rights reserved. A significant part of this research work was conducted during the first author's attachment to the HLT department of I2R in Singapore. The work of the first and third authors was carried out in the framework of the WIQ-EI IRSES project (Grant no. 269180) within the FP 7 Marie Curie, the DIANA APPLICATIONS Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.

Current approaches to single and cross-domain polarity classification usually use bag of words, n-grams or lexical resource-based classifiers. In this paper, we propose the use of meta-learning to combine and enrich those approaches by adding also other knowledge-based features. In addition to the aforementioned classical approaches, our system uses the BabelNet multilingual semantic network to generate features derived from word sense disambiguation and vocabulary expansion. Experimental results show state-of-the-art performance on single and cross-domain polarity classification. Contrary to other approaches, ours is generic. These results were obtained without any domain adaptation technique. Moreover, the use of meta-learning allows our approach to obtain the most stable results across domains. Finally, our empirical analysis provides interesting insights on the use of semantic network-based features. Ministerio de Economía y Competitividad TIN2012-38603-C02-01 Ministerio de Economía y Competitividad TIN2012-38536-C03-02 European Comission WIQ-EI IRSES (No. 269180) Junta de Andalucía P11-TIC-7684 MO

In this paper we introduce and discuss a concept of syntactic n-grams (sn-grams). Sn-grams differ from traditional n-grams in the manner how we construct them, i.e., what elements are considered neighbors. In case of sn-grams, the neighbors are taken by following syntactic relations in syntactic trees, and not by taking words as they appear in a text, i.e., sn-grams are constructed by following paths in syntactic trees. In this manner, sn-grams allow bringing syntactic knowledge into machine learning methods; still, previous parsing is necessary for their construction. Sn-grams can be applied in any natural language processing (NLP) task where traditional n-grams are used. We describe how sn-grams were applied to authorship attribution. We used as baseline traditional n-grams of words, part of speech (POS) tags and characters; three classifiers were applied: support vector machines (SVM), naive Bayes (NB), and tree classifier J48. Sn-grams give better results with SVM classifier.

The last decade had known a great interest in Arabic Natural Language Processing (NLP) applications. This interest is due to the prominent importance of this 6th most wide-spread language in the world with more than 350 million native speakers. Currently, some basic Arabic language challenges related to the high inflection and derivation, Part-of-Speech (PoS) tagging, and diacritical ambiguity of Arabic text are practically tamed to a great extent. However, the development of high level and intelligent applications such as Question Answering (QA) systems is still obstructed by the lacks in terms of ontologies and other semantic resources. In this paper, we present the construction of a new Arabic ontology leveraging the contents of Arabic WordNet (AWN) and Arabic VerbNet (AVN). This new resource presents the advantage to combine the high lexical coverage and semantic relations between words existing in AWN together with the formal representation of syntactic and semantic frames corresponding to verbs in AVN. The Conceptual Graphs representation was adopted in the framework of a multi-layer platform dedicated to the development of intelligent and multi-agents systems. The built ontology is used to represent key concepts in questions and documents for further semantic comparison. Experiments conducted in the context of the QA task show a promising coverage with respect to the processed questions and passages. The obtained results also highlight an improvement in the performance of Arabic QA regarding the c@1 measure. The work of the last author was carried out in the framework of the WIQ-EI IRSES project (Grant No. 269180) within the FP 7 Marie Curie, the DIANA APPLICATIONS - Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) project, and the VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems.

[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.