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[eu_members at aclweb dot org] California: NAACL HLT 2010 Workshop on Active Learning for Natural Language Processing (ALNLP) -- CFP

                    First Call for Paper Submissions

                    NAACL-HLT 2010 Workshop on
Active Learning for Natural Language Processing (ALNLP)

              June 5 or 6, 2010, Los Angeles, CA


               Submission Deadline: March 1, 2010

Labeled training data is required to achieve state-of-the-art performance for 
many machine learning solutions to NLP tasks.  While traditional supervised 
methods rely exclusively on existing labeled data to induce a model, active 
learning allows the learner to select unlabeled data for labeling in an effort to 
reduce annotation costs without sacrificing performance.  Thus, active learning 
appears promising for NLP applications where unlabeled data is readily 
available (e.g., web pages, audio recordings, minority language data), but 
obtaining labels is cost-prohibitive.

Ample recent work has demonstrated the effectiveness of active learning over
a diverse range of applications.  Despite these findings, active learning has 
not yet been widely adopted for many ongoing large-scale corpora annotation 
efforts -- resulting in a dearth of real-world case studies and copious research
questions.  Machine learning literature has primarily focused on active learning 
in the context of classification, devoting less attention to issues specific to NLP 
including annotation user studies, incorporation of semantic information, and
more complex prediction tasks (e.g. parsing, machine translation).


The aim of this workshop is to foster innovation and discussion that advances
our understanding in these and other practical issues for active learning in NLP.
Topics of particular interest include:

  -- Alternative query types: labeling features rather than instances, 
     mixed-resolution queries for structured instances, etc.
  -- Creative ways for obtaining data via active learning (e.g., online games, 
      Mechanical Turk)
  -- Managing multiple, possibly non-expert annotators (e.g., "crowdsourcing" 
  -- Reusability: using data acquired with one active learner to train other 
      model classes
  -- Domain adaptation and active learning
  -- Multi‐task active learning
  -- Criteria for stopping and monitoring active learning progress
  -- Active learning in coordination with semi-supervised or unsupervised 
      learning approaches
  -- Interactive active learning interfaces and other HCI issues
  -- Parallelization of active learning and its computational challenges
  -- Software engineering considerations for active learning and NLP
  -- Theoretical analysis of active learning

We also welcome case-study papers describing the application of active 
learning in real-world annotation projects and lessons learned thereby.  
Additionally, we would consider papers with insights applicable to NLP from 
other machine learning communities (e.g., computer vision, bioinformatics, 
and data mining), where annotation costs are also high. 


We invite submissions of two kinds:  1. original and unpublished work as
full papers, limited to 8 pages (+1 extra page for references); 2. position or 
work-in-progress papers, limited to 4 pages (including references).  Both kinds 
of papers will appear in the proceedings and presented orally.  As reviewing 
will be double-blind, author information should not be included in the papers 
and self-reference should be avoided.

All submissions must be made in PDF format using the START paper 
submission website:
Submissions must follow the NAACL HLT 2010 formatting requirements:
Authors are strongly encouraged to use the LaTeX or Microsoft Word style
files available there.  Papers not conforming to these requirements are
subject to rejection without review.


March 1, 2010: Paper Submission Deadline
March 30, 2010: Notification of acceptance
June 5 or 6, 2010: Workshop held in conjunction with NAACL-HLT


 - Burr Settles, Carnegie Mellon University, USA
 - Kevin Small, Tufts University, USA
 - Katrin Tomanek, University of Jena, Germany

Please address any queries regarding the workshop to:
             alnlp2010 at gmail dot com


 - Markus Becker (SPSS, UK)
 - Claire Cardie (Cornell University, USA)
 - Hal Daume III (University of Utah, USA)
 - Ben Hachey (University of Edinburgh, UK)
 - Robbie Haertel (Brigham Young University, USA)
 - Udo Hahn (University of Jena, Germany)
 - Eric Horvitz (Microsoft Research, USA)

 - Rebecca Hwa (University of Pittsburgh, USA)
 - Ashish Kapoor (Microsoft Research, USA)
 - Prem Melville (IBM T.J. Watson Research Center, USA)

 - Ray Mooney (University of Texas at Austin, USA)
 - Fredrik Olsson (SICS, Sweden)
 - Foster Provost (New York University, USA)
 - Eric Ringger (Brigham Young University, USA)
 - Dan Roth (University of Illinois at Urbana-Champaign, USA)
 - Burr Settles (Carnegie Mellon University, USA)
 - Kevin Small (Tufts University, USA)
 - Katrin Tomanek (University of Jena, Germany)