1st CALL FOR PAPERS SIGIR'07 Workshop PAN Plagiarism Analysis, Authorship Identification, and Near-Duplicate Detection -- http://www.aisearch.de/pan-07 -- In conjunction with the 30th Annual International ACM SIGIR Conference on Research & Development on Information Retrieval, Amsterdam, 23-27 July 2007. ---------------------------------------------------------- ABOUT THIS WORKSHOP: The workshop shall bring together experts and prospective researchers around the exciting and future-oriented topic of plagiarism analysis, authorship identification, and high similarity search. This topic receives increasing attention, which results, among others, from the fact that information about nearly any subject can be found on the World Wide Web. At first sight, plagiarism, authorship, and near-duplicates may pose very different challenges; however, they are closely related in several technical respects. Plagiarism analysis is a collective term for computer-based methods to identify a plagiarism offense. In connection with text documents we distinguish between corpus-based and intrinsic analysis: the former compares suspicious documents against a set of potential original documents, the latter identifies potentially plagiarized passages by analyzing the suspicious document with respect to changes in writing style. Authorship identification divides into so-called attribution and verification problems. In the authorship attribution problem, one is given examples of the writing of a number of authors and is asked to determine which of them authored given anonymous texts. In the authorship verification problem, one is given examples of the writing of a single author and is asked to determine if given texts were or were not written by this author. Authorship verification and intrinsic plagiarism analysis represent two sides of the same coin. Near-duplicate detection is mainly a problem of the World Wide Web: duplicate Web pages increase the index storage space of search engines, slow down result serving, and decrease the retrieval precision. Near-duplicate detection relates directly to plagiarism analysis: at the document level, near-duplicate detection and plagiarism analysis represent also two sides of the same coin. For a plagiarism analysis at the paragraph level, the same specialized document models (e.g. shingling, fingerprinting, hashing) can be applied, where a key problem is the selection of useful chunks from a document. The development of new solutions for the outlined problems may benefit from the combination of existing technologies, and in this sense the workshop provides a platform that spans different views and approaches. The following list gives examples from the outlined field for which contributions are welcome (but not restricted to): - retrieval models for plagiarism analysis, authorship identification, and style analysis - software plagiarism, cross-language plagiarism, plagiarism in Web communities and social networks - NLP technologies for authorship identification and style analysis - knowledge-based methods for plagiarism analysis and authorship identification - handling proper citation - methods for identifying near-duplicate and versioned documents (for all kinds of contents, including text, source code, image, and music documents) - shingling, fingerprinting, and similarity hashing - hash-based search, high-dimensional search, approximate nearest neighbor search - efficiency issues and performance tradeoffs - tailored indexes for plagiarism analysis and near-duplicate detection - plagiarism analysis and near-duplicate detection on the Web - evaluation, building of test collections, experimental design and user studies IMPORTANT DATES: Deadline for paper submission May 27, 2007 Notification to authors June 24, 2007 Camera-ready copy due July 1, 2007 Workshop opens July 27, 2007 Contributions will be peer-reviewed by experts from the related field. WORKSHOP ORGANIZATION: Benno Stein, Bauhaus University Weimar Moshe Koppel, Bar-Ilan University, Israel Efstathios Stamatatos, University of the Aegean Contact: pan-07@aisearch.de URL: http://www.aisearch.de/pan-07 PROGRAM COMMITTEE: Shlomo Argamon, Illinois Institute of Technology Yaniv Bernstein, Google Switzerland Dennis Fetterly, Microsoft Research Graeme Hirst, University of Toronto Timothy Hoad, Microsoft Heiko Holzheuer, Lycos Europe Jussi Karlgren, Swedish Institute of Computer Science Hans Kleine Büning, University of Paderborn Moshe Koppel, Bar-Ilan University, Israel Hermann Maurer, University of Technology Graz Sven Meyer zu Eissen, Bauhaus University Weimar Efstathios Stamatatos, University of the Aegean Benno Stein, Bauhaus University Weimar Özlem Uzuner, State University of New York Debora Weber-Wulff, University of Applied Sciences Berlin Justin Zobel, RMIT University