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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.

Invited Talk

"Grammatical Inference with String Kernels"
Alexander Clark, Royal Holloway University of London
Sponsored by the Pascal Network.

Submission Information

Prospective authors are invited to email their 8-page papers to cagi07@cs.okstate.edu 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@cs.okstate.edu

Important Dates

• Paper Submission May 7, 2007. Closed.
  
• Acceptance Notification May 25, 2007. Sent.
  
• Electronic Proceedings June 15, 2007. Online
  
• Workshop date June 24, 2007.

Organizing Committee

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

Program Committee

• Pierre Dupont (PC chair), Université catholique de Louvain, Belgium
  
• Pieter Adriaans, Universiteit van Amsterdam, The Netherlands
  
• Istvan Jonyer, Oklahoma State University, USA
  
• Laurent Miclet, Université de Rennes, France
  
• Tim Oates, University of Maryland Baltimore County, USA
  
• Rajesh Parekh, Yahoo!, Inc., USA
  
• Yasubumi Sakakibara, Keio University, Japan
  
• Marc Sebban, Université de Saint-Etienne, France
• Menno van Zannen, Macquarie University, Australia
  
• Enrique Vidal, Universidad Politécnica de Valencia, Spain

Sponsored by: