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Journal of Natural Language Engineering
Special issue on computational approaches to the semantics of noun compounds
Call for papers
(apologies for multiple postings)
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Noun compounds are a major challenge for the automatic analysis of English written text. A noun compound is a sequence of nouns acting as a single noun, such as "colon cancer tumor suppressor protein" or "carbon steel soup pot cover". Often at least partially lexicalised, such constructions encode implicit relations which tend to be hard for language processing software to understand. For example, olive oil *is extracted from* olives, while malaria mosquito *spreads* malaria. Noun compounds are abundant in written English. They comprise 3.9% of the tokens in the Reuters corpus and 2.6% in the British National Corpus, so they cannot be conveniently ignored. They also are highly productive: over half of the two-noun compound types in the BNC occur only once. Moreover, noun compounds cannot be enumerated in any static resource: it has been shown that static English dictionaries cover only 27% of the noun compounds that occur 10+ times in the BNC.
It is not surprising that noun compounds have attracted a lot of research interest in theoretical linguistics and in computational linguistics. There has been considerable progress in the theory and practice of their semantic interpretation in the last several years, as well as new insights into the process of compounding and its use in text processing applications. For example, a question-answering system might need to determine whether "protein acting as a tumor suppressor" is a good paraphrase for "tumor suppressor protein". An information extraction system might need to decide whether "neck vein thrombosis" and "neck thrombosis" could co-refer when used in the same document. A machine translation system facing the unknown noun compound "WTO Geneva headquarters" might benefit from being able to paraphrase it as "Geneva headquarters of the WTO" or "WTO headquarters located in Geneva". Given a query like "migraine treatment", an information retrieval system could use paraphrasing verbs like "relieve" and "prevent" for page ranking and query refinement.
We invite contributions on topics related to computational approaches to the semantics of noun compounds, including but not limited to the following areas:
- designing models, resources and tools for the syntactic and semantic interpretation of noun compounds;
- comparing and mapping between different semantic representations;
- evaluating the quality of noun compound interpretation systems;
- paraphrasing noun compounds;
- adapting linguistic theories to the computational interpretation of noun compounds;
- applying noun compound interpretation to various natural language processing tasks.
We seek original unpublished papers of no more than 20 pages in the JNLE format. Submission details will be announced closer to the deadline.
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Important dates
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First call for papers: December 7, 2010
Submissions: October 1, 2011
Initial decisions: January 1, 2012
Submission of revised versions: May 1, 2012
Final decisions: August 1, 2012
Submission of camera-ready versions: November 1, 2012
Publication: after January 2013
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Guest editors
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Francis Bond, Nanyang Technological University, Singapore
Su Nam Kim, The University of Melbourne, Australia
Preslav Nakov, National University of Singapore, Singapore
Stan Szpakowicz, University of Ottawa, Canada
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Guest editorial board
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Timothy Baldwin, University of Melbourne, Australia
Ann Copestake, University of Cambridge, UK
Ido Dagan, Bar Ilan University, Israel
Roxana Girju, University of Illinois at Urbana-Champaign, USA
Gregory Grefenstette, Exalead S.A, France
Chikara Hashimoto, National Institute of Information and Communications Technology, Japan
Iris Hendrickx, University of Lisboa, Portugal
Kyo Kageura, University of Tokyo, Japan
Zornitsa Kozareva, University of Southern California, USA
Valia Kordoni, University of Saarland, Germany
Alex Lascarides, University of Edinburgh, UK
Diana McCarthy, University of Sussex, UK
Dan Moldovan, Univeristy of Texas at Dallas, USA
Sebastian Pado, Heidelberg University, Germany
James Pustejovsky, Brandeis University, USA
Diarmuid Ó Séaghdha, University of Cambridge, UK
Lorenza Romano, FBK-irst, Italy
Barbara Rosario, Intel Lab, USA
Koichi Takeuchi, Okayama University, Japan
Peter Turney, National Research Council, Canada
Lucy Vandewende, Microsoft Research, USA
Aline Villavicencio, Federal University of Rio Grande do Sul, Brasil
Deniz Yuret, Koç University, Turkey