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[eu_members at aclweb dot org] ACM TIST Special Issue on Domain Adaptation in Natural Language Processing -- CFP

                           Call for Papers

   ACM Transactions on Intelligent Systems and Technology (ACM TIST)

 = Special Issue on Domain Adaptation in Natural Language Processing =
Full Paper Submission Deadline: June 1, 2010
Review Notification:            September 1, 2010
Final Manuscript:               November 1, 2010
Publication Date:               December 2010

Topics of Interest

Over the past two decades, supervised learning methods have been
successfully applied to many natural language processing problems such
as syntactic parsing, information extraction and machine translation.
However, a major drawback of supervised learning methods is their heavy
reliance on the quality and size of annotated training corpora, which
are highly labor-intensive to create. It is well understood that when
test data comes from a different domain and thus has a different
distribution than the training data, performance of learning-based
systems can drop substantially. In natural language processing, this
domain adaptation problem has been reported for various tasks including
word sense disambiguation, parsing, named entity recognition and
sentiment analysis, to name just a few. Although this is a fundamental
problem with statistical learning, it only started gaining much
attention in recent years.

The objective of this special issue is to provide a venue to highlight
some of the recent advances in developing domain adaptive techniques for
natural language processing and related areas such as information
retrieval and text mining, with an emphasis on applications and systems.
Topics of interest include but are not limited to

* novel domain adaptation techniques and applications designed
with a focus on NLP problems
* evaluation of general domain adaptation systems applied to
specific NLP problems
* adaptation of NLP tools to handle noisy text data such as email
and blogs
* cross-lingual adaptation techniques and systems
* analysis and comparison between domain adaptation and other
related problems such as semi-supervised learning and active learning
for NLP problems
* techniques and systems for measuring domain relatedness and
learning from multiple domains in NLP
* domain adaptive NLP techniques applied to multi-disciplinary
domains such as medicine and bioinformatics areas


On-Line Submission (will be available before June 1, 2010):
http://mc.manuscriptcentral.com/tist (please select "Special Issue:
Domain Adaptation in Natural Language Processing" as the manuscript

Details of the journal and manuscript preparation are available on the

Each paper will be peer-reviewed by at least three reviewers.

Important Dates

Full Paper Submission Deadline: June 1, 2010
Review Notification: September 1, 2010
Final Manuscript: November 1, 2010
Publication Date: December 2010

Guest Editors

Hal Daume III (University of Utah)
Jing Jiang (Singapore Management University), Special Issue Contact
(jingjiang at smu dot edu dot sg)
Sinno Jialin Pan (Hong Kong University of Science and Technology)
Masashi Sugiyama (Tokyo Institute of Technology)