Machine Learning Journal Special Issue on Natural Language Learning
Call for Papers

The application of learning techniques to natural language processing has grown dramatically in recent years under the rubric of "corpus-based," "statistical," or "empirical" methods. However, most of this research has been conducted outside the traditional machine learning research community. This special issue is an attempt to bridge this divide by inviting researchers in all areas of natural language learning to communicate their recent results to a general machine learning audience. Papers are invited on learning applied to all natural language tasks including: and all learning approaches including: Experimental papers with significant results evaluating either engineering performance or cognitive-modeling validity on suitable corpora are invited. Papers will be evaluated by three reviewers, including at least two experts in the relevant area of natural language learning; however, they should be written to be reasonably accessible to a general machine learning audience.

Schedule:

December 1, 1997: Deadline for submissions. PAST
April 1, 1998: Deadline for getting decisions back to authors. PAST
June 1, 1998: Deadline for authors to submit final versions.
Winter 1998/99: Publication

Submission Guidelines:

  1. Manuscripts should conform to the formatting instructions in:
    http://www.cs.orst.edu/~tgd/mlj/info-for-authors.html
    The first author will be the primary contact unless otherwise stated.

  2. Authors should send 5 copies of the manuscript to:

    Ms. Amy Beaudoin
    Machine Learning Editorial Office
    Attn: Special Issue on Natural Language Learning
    Kluwer Academic Press
    101 Philip Drive
    Assinippi Park
    Norwell, MA 02061
    617-871-6300
    617-871-6528 (fax)
    abeaudoin@wkap.com

    and one copy to:

    Raymond J. Mooney
    Department of Computer Sciences
    Taylor Hall 2.124
    University of Texas
    Austin, TX 78712-1188
    (512) 471-9558
    (512) 471-8885 (fax)
    mooney@cs.utexas.edu

  3. Please also send an ASCII title page (title, authors, email, abstract, and keywords) and a postscript version of the manuscript to mooney@cs.utexas.edu.

General Inquiries:

Please address general inquiries to: mooney@cs.utexas.edu

Co-Editors:

Claire Cardie, Cornell University, cardie@cs.cornell.edu
Raymond J. Mooney, University of Texas at Austin, mooney@cs.utexas.edu
mooney@cs.utexas.edu