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CFP: Grammar Induction Workshop at ICML'07

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  • I Jonyer
    ... Call for participation ICML-2007 Workshop on Challenges and Applications of Grammar Induction In conjunction with the International Conference on Machine
    Message 1 of 5 , Mar 30, 2007
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      Call for participation
      ICML-2007 Workshop on
      Challenges and Applications of Grammar Induction

      In conjunction with the International Conference on Machine Learning,
      Oregon State University, June 20 - June 24, 2007

      Sponsored by the Pascal Network
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      Description

      Grammar Induction (GI), also known as Grammatical Inference, is about
      learning grammars from data. A well-known important application of GI
      is natural language learning, but it is applicable in a much broader
      sense to the problem of learning structural models from data. The data
      typically consists of sequences of discrete events from various domains
      (such as text, DNA fragments, primary structure of proteins, sequential
      process log-files and musical scores), but can also include trees and
      arbitrary graphs (such as metabolic networks and social networks).
      Typical models include formal grammars (regular, context-free, context-
      sensitive, . . .), and statistical models in related formalisms such as
      probabilistic automata, hidden Markov models, probabilistic transducers
      or conditional random fields.

      The CAGI workshop aims at highlighting current challenges in grammar
      induction with a special focus on applicability issues including:
      - practical evaluations demonstrating the usefulness of the proposed
      techniques,
      - novel applications of grammar induction algorithms,
      - noise resistant approaches,
      - semi-supervised grammar learning,
      - learning from partial sequences or streams,
      - approximate induction and model optimization,
      - experimental assessments illustrating the current limit(s) of the GI
      field,
      - practical complexity and scalability issues (alphabet size, noise
      level, data sparseness, data inconsistency, . . .),
      - evaluation of similarity learning algorithms from structured data
      (pair-HMM learning, stochastic transducer learning, . . .).


      Workshop Format

      The workshop will include presentations of peer-reviewed papers. Each
      such paper will be assigned 30 minutes, including 10 minutes for
      discussion. Each half-day will start with an invited paper for 45
      minutes including the discussion. The day will be concluded with an
      open panel for discussing the key lessons learned and pointing at
      relevant research perspectives.


      Submission Information

      Prospective authors are invited to email their 8-page papers to
      cagi07@... by the due date in PDF format. Formatting
      instructions are given by the conference at
      http://oregonstate.edu/conferences/icml2007/icml_format_2007.zip.
      The workshop will not have a blind review process, and therefore
      author names, affiliations, and contact information should appear in
      the submission, including postal address, email address, telephone
      number, and fax number. Electronic versions of the final papers will
      be available on the workshop home page at www.cs.okstate.edu/cagi07/.

      Interested participants are also invited to submit 2-page position
      papers. These will also be peer-reviewed and appear in the workshop
      proceedings. If the workshop schedule allows, short presentations
      at the end of the day may be possible as well.

      Workshop home page: www.cs.okstate.edu/cagi07/
      Submit papers to: cagi07@...


      Important Dates

      Paper Submission May 7, 2007
      Acceptance Notification May 25, 2007
      Electronic Proceedings June 15, 2007
      Workshop date June 24, 2007


      Organizing Committee

      Istvan Jonyer, Oklahoma State University, USA
      Pierre Dupont, Universit� catholique de Louvain, Belgium
      Tim Oates, University of Maryland Baltimore County, USA
      Marc Sebban, Universit� de Saint-Etienne, France


      Program Committee

      Pierre Dupont (PC chair), Universit� catholique de Louvain, Belgium
      Pieter Adriaans, Universiteit van Amsterdam, The Netherlands
      Vasant Honavar, Iowa State University, USA
      Istvan Jonyer, Oklahoma State University, USA
      Laurent Miclet, Universit� de Rennes, France
      Tim Oates, University of Maryland Baltimore County, USA
      Rajesh Pareck, Iowa State University, USA
      Yasubumi Sakakibara, Keio University, Japan
      Marc Sebban, Universit� de Saint-Etienne, France
      Menno van Zannen, Macquarie University, Australia
      Enrique Vidal, Universidad Polit�cnica de Valencia, Spain

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