Cross-Language Plagiarism Detection Using a Multilingual Semantic Network

Abstract

Gupta, PA.; Rosso ., P. (2013). Cross-language plagiarism detection using multilingual semantic network. En Advances in Information Retrieval. Springer Verlag (Germany). 7814:710-713. doi:10.1007/978-3-642-36973-5_66. Cross-Language Plagiarism Detection using a Multilingual Semantic NetworkMarc Franco-Salvador, Parth Gupta, and Paolo Rosso Natural Language Engineering Lab -ELiRF, DSIC Universitat Politècnica de València, Valencia, Spain {mfranco,pgupta,prosso}@dsic.upv.esAbstract. Cross-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 detection.

Word Embedding-Based Approaches for Measuring Semantic Similarity of Arabic-English Sentences

“…For that, a vector of concepts is built for each textual unit using dictionaries or thesaurus. The similarity between the vectors of concepts can be measured using the Cosine similarity, Euclidean distance, or any MT-Based Models Kent and Salim [18], Muhr et al [29], SS-CL-AES [3], CL-PDAE [2] Comparable Corpora-Based Models CL-KGA [11], CL-ESA [12] Parallel Corpora-Based Models CL-ASA [6], CL-LSI [35], CL-KCCA [42], CL-AE-LSI [17] Dictionary-Based Models CL-CTS [15], CL-DBLI [32], CL-PDAE [2] Syntax-Based Models Length Model [16], CL-CNG [22] Fig. 1: Taxonomy of different approaches for cross-language similarity detection [10].…”

“…In a cross-lingual context, Potthast et al [36] use Wikipedia as comparable corpus to estimate the similarity of two documents by calculating the similarity of their two ESA representations. Another model called Cross-Language Knowledge Graph Analysis (CL-KGA), is introduced for the first time by Franco-Salvador et al [11]. CL-KGA uses knowledge graphs built from multilingual semantic network (the authors use BabelNet [31]) to represent texts, and then compare them in a common lingual semantic graph space.…”

Author Profiling and Plagiarism Detection

“…Evaluation Corpora In the author profiling task at PAN 2013 [58] participants approached the task of identifying age and gender in a large corpus collected from social media, and age was annotated with three classes: 10s (13)(14)(15)(16)(17), 20s (23)(24)(25)(26)(27), and 30s (33)(34)(35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47). At PAN 2014, we continued to study the gender and age aspects of the author profiling problem, however, four corpora of different genres were considered-social media, blogs, Twitter, and hotel reviews-both in English and Spanish.…”

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

“…Our approach, cross-language knowledge graphs analysis (CL-KGA), presented previously in [5,6], uses knowledge graphs generated from a MSN to obtain a context model of document fragments in different languages. The similarities between document fragments are computed in a semantic graph space.…”