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:

Syntax: part-of-speech tagging, parsing, language modeling, prepositional-phrase attachment, spelling correction, word segmentation
Semantics: Word-sense disambiguation, word clustering, lexicon acquisition, semantic analysis, database-query mapping
Discourse: Information extraction, anaphora resolution, discourse segmentation
Machine Translation: Bilingual text alignment, bilingual dictionary construction, lexical, syntactic, and semantic transfer

and all learning approaches including:

Statistical: n-gram models, hidden Markov models, probabilistic context-free grammars, Bayesian networks
Symbolic: Decision trees, rule-based, case-based, inductive logic programming, automata and grammar induction
Neural-Network & Evolutionary: recurrent networks, self-organizing maps, genetic algorithms

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:

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.
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
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

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

Schedule:

Submission Guidelines:

General Inquiries:

Co-Editors:

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