Call for Paper Submissions

<big>NAACL HLT 2009 Workshop on Active Learning for Natural Language Processing</big>

June 5, 2009, Boulder, Colorado, USA

Submission Deadline: EXTENDED TO March 10, 2009 (23:59 GMT-12)

Endorsed by the following ACL Special Interest Groups

  • SIGNLL, Special Interest Group for Natural Language Learning
  • SIGANN, Special Interest Group for Annotation


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.


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


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:

Submissions must follow the NAACL HLT 2009 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.

Important Dates

  • EXTENDED TO March 10, 2009 (23:59 GMT-12): 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: []

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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nlp/workshop-on-active-learning-for-nlp-cfp.txt · Last modified: 2015/04/21 16:09 by ryancha
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