[Corpora-List] Second Call for Papers: Workshop on Active Learning for NLP at NAACL 2009

[Katrin Tomanek]

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                    NAACL HLT 2009 Workshop on
           Active Learning for Natural Language Processing

               June 5, 2009, Boulder, Colorado, USA

                     http://nlp.cs.byu.edu/alnlp/

                  Submission Deadline: March 6, 2009

          Endorsed by the following ACL Special Interest Groups:
          * Special Interest Group on Natural Language Learning (SIGNLL)
          * Special Interest Group for Annotation (SIGANN)

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MOTIVATION

Labeled data is a prerequisite for many popular algorithms in natural 
language processing and machine learning.  While it is possible to 
obtain large amounts of annotated data for well-studied languages in 
well-studied domains and well-studied problems, labeled data are rarely 
available for less common languages, domains, or problems. 
Unfortunately, obtaining human annotations for linguistic data is 
labor-intensive and typically the costliest part of the acquisition of 
an annotated corpus.

It has been shown before that active learning can be employed to reduce 
annotation costs but not at the expense of quality.  While diverse work 
over the past decade has demonstrated the possible advantages of active 
learning for corpus annotation and NLP applications, active learning is 
not widely used in many ongoing data annotation tasks.  Much of the 
machine learning literature on the topic has focused on active learning 
for classification problems with less attention devoted to the kinds of 
problems encountered in NLP.


TOPICS

We are interested in bringing together researchers to explore the 
challenges and opportunities of active learning for NLP tasks, language 
acquisition, and language learning.  General work on active learning on 
NLP classification tasks, sequence labeling, parsing, semantics, and 
other more complex tasks will be welcome in the workshop.  More specific 
topics of interest include, but are not limited to:

   - theoretical analysis of active learning in the context of NLP
     applications
   - novel active learning approaches to estimate the training utility
     of individual selection units
   - cost-sensitive active learning approaches incorporating data
     acquisition costs
   - approaches to model or predict annotation costs as well as studies
     on factors that influence annotation time
   - criteria for stopping or monitoring progress of active learning
   - overfitting of data acquired with active learning: how much is the
     data biased towards the learning scheme involved in the selection
     and what are the limitations of re-use with other learning schemes
   - Human-Computer Interaction aspects of annotation including
     requirements, impact of interface design on annotation time, and
     methods to deal with reliability of annotators
   - approaches to multi-task active learning
   - approaches to deal with or reduce computational complexity of
     active learning approaches including parallelization, issues of
     pool- or batch-size, varying degrees of look-ahead, etc.
   - active learning and domain adaption
   - active learning compared to or combined with other semi-supervised
     or even unsupervised learning approaches
   - application of active learning in real annotation projects and
     experiences gained thereby


SUBMISSIONS

We invite submissions of two kinds:
1. original and unpublished work as full papers, limited to 8 pages of 
text (up to one extra page may be used for references);
2. position papers or papers describing ongoing work as short papers, 
limited to 4 pages in total (including references).

Both kinds of papers will appear in the proceedings and will be 
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:
https://www.softconf.com/naacl-hlt09/ActiveLearningNLP2009/

Submissions must follow the NAACL HLT 2009 formatting requirements:
http://clear.colorado.edu/NAACLHLT2009/stylefiles.html

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.


IMPORTANT DATES

March 6, 2009: Submission Deadline
March 30, 2009: Notification of acceptance
April 12, 2009: Camera-ready copies due
June 5, 2009: Workshop held in conjunctions with NAACL HLT


ORGANIZERS AND CONTACT

   - Eric Ringger, Brigham Young University, USA
   - Robbie Haertel, Brigham Young University, USA
   - Katrin Tomanek, University of Jena, Germany

Please address any queries regarding the workshop to:
               al.nlp2009 at googlemail.com


PROGRAM COMMITTEE

   - Shlomo Argamon (Illinois Institute of Technology, USA)
   - Jason Baldridge (University of Texas at Austin, USA)
   - Markus Becker (SPSS, UK)
   - Ken Church (Microsoft Research, USA)
   - Hal Daume (University of Utah, USA)
   - Robbie Haertel (Brigham Young University, USA)
   - Ben Hachey (University of Edinburgh, UK)
   - Udo Hahn (University of Jena, Germany)
   - Eric Horvitz (Microsoft Research, USA)
   - Rebecca Hwa (University of Pittsburgh, USA)
   - Ashish Kapoor (Microsoft Research, USA)
   - Mark Liberman (University of Pennsylvania/LDC, USA)
   - Prem Melville (IBM T.J. Watson Research Center, USA)
   - Ray Mooney (University of Texas at Austin, USA)
   - Miles Osborne (University of Edinburgh, UK)
   - Eric Ringger (Brigham Young University, USA)
   - Kevin Seppi (Brigham Young University, USA)
   - Burr Settles (University of Wisconsin, USA)
   - Victor Sheng (New York University, USA)
   - Katrin Tomanek (University of Jena, Germany)
   - Jingbo Zhu (Northeastern University, China)

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