Knowledge Graphs as Context Models: Improving the Detection of Cross-Language Plagiarism with Paraphrasing
| Autor: | Paolo Rosso, Marc Franco-Salvador, Parth Gupta |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2013 |
| Předmět: |
Structure (mathematical logic)
Information retrieval Copying business.industry Computer science 05 social sciences Context (language use) 02 engineering and technology BabelNet computer.software_genre Textual similarity Cross-language plagiarism detection Knowledge graph Knowledge graphs 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Plagiarism detection Artificial intelligence 0509 other social sciences 050904 information & library sciences business computer LENGUAJES Y SISTEMAS INFORMATICOS Natural language processing Paraphrasing |
| Zdroj: | RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia instname Lecture Notes in Computer Science ISBN: 9783642547973 PROMISE Winter School |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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