CFP: SIGIR'07 Workshop PAN. Plagiarism Analysis, Authorship Identification, Near-Duplicate Detection
st...@upb.de
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.
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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 22, 2007
Camera-ready copy due July 3, 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...@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 Buening, 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
Ozlem Uzuner, State University of New York
Debora Weber-Wulff, University of Applied Sciences Berlin
Justin Zobel, RMIT University
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