[Corpora-List] CFP: Machine Learning Journal Special Issue on Learning in Speech and Language Technologies

[Pascale Fung]

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(CFP can be found on line at
http://www.ee.ust.hk/~pascale/MLJspecial.html)

Machine Learning Journal
Special Issue on Learning in Speech and Language Technologies
CALL FOR PAPERS

Machine learning techniques have long been the foundations of speech
processing. Bayesian classification, decision trees, unsupervised
clustering,the EM algorithm, maximum entropy, etc. are all part of
existing speech recognition systems. Meanwhile, the success of
statistical speech recognition has led to the rise of statistical and
empirical methods in natural language processing.

Many of the machine learning techniques in language processing, from
statistical part-of-speech tagging to the noisy channel model for
machine translation have roots in work conducted in the speech field. In
turn, advances in Learning Theory and algorithmic Machine Learning
approaches have also made a mark on natural language and speech processing.
Approaches such as memory based learning, a range of linear classifiers
such as Boosting, SVMs and SNoW and others have been successfully
applied to a broad range of natural language problems, and these now
inspire new research in speech retrieval and recognition. We have seen
an increasingly close collaboration between voice and language
processing researchers in some of the shared tasks such as spontaneous
speech recognition and understanding, voice data information extraction,
and machine translation.

The purpose of this special issue is to invite speech and language
researchers to communicate with each other, and with the machine
learning community on the latest machine learning advances in their
work. We hope to promote both the development of new theoretical
frameworks and of further application of machine learning techniques in
new ways to both speech and language areas, fueling the synergy between
the two.

Papers are invited on learning applied to all speech and natural
language tasks including, but not limited to:

Acoustics & Phonetics, Syntax, Semantics, Discourse and Dialog, Language
Modeling, Spoken Language Understanding and Generation, Multilingual
Processing, Machine Translation, Spoken Language Information Extraction
and Retrieval, Natural Language and Spoken Language based Interactive
Systems.

We welcome work within any machine learning and statistical frameworks
and/or the development of a new framework for any of the above areas.

Original theoretical or experimental papers showing significant
contribution in the above areas are invited. Papers showing the synergy
between speech and language processing using learning are especially
encouraged. Papers will be evaluated by experts in the relevant area of
natural language learning, but should be written to be reasonably
accessible to a general machine learning audience.

Co-Editors:

Pascale Fung (pascale at ee.ust.hk) (University of Science & Technology,
HKUST)
Dan Roth (danr at cs.uiuc.edu) (University of Illinois at Urbana/Champaign)

Advisory/Editorial Board:


Eric Brill (Microsoft Research)
Ken Church (AT&T Research)
Walter Daelemans  (University of Antwerp)
Mark Hasegawa-Johnson (University of Illinois at Urbana/Champaign)
Eric Fosler-Lussier  (Ohio State University)
Frederick Jelinek (Johns Hopkins University)
Lillian Lee (Cornell University)
Christopher Manning (Stanford University)
Yuji Matsumoto (Nara Institute of Science & Technology)
Mehryar Mohri (AT&T Research)
Hwee Tou Ng (National University of Singapore)
Roberto Pieraccini (Speech Works International)
Richard Schwartz  (BBN Technologies)
Richard Sproat  (University of Illinois at Urbana/Champaign)
Dekai Wu (University of Science & Technology, HKUST)

Schedule:

October 1, 2003: Deadline for submissions.
December 15, 2003: Deadline for getting decisions back to authors.
March 15, 2004: Deadline for authors to submit final versions.
Fall 2004: Publication

Submission Guidelines:

(1) Format:
Manuscripts should conform to the formatting instructions in:
http://www.cs.ualberta.ca/~holte/mlj/info-for-authors.html
Please limite your submission to approximately less than 8000 words.

(2) Electronic submission:

Submission instructions are available here.

However, in addition to everything stated there, for papers submitted to
special issues:

     -send an email with title page to pascale at ee.ust.hk with paper
title and author information. The first author will be the primary
contact unless otherwise stated.
     -state clearly in the body of the email submission that it is for
THIS special issue
     -copy all submissions to Kluwer to danr at cs.uiuc.edu
     -please make sure you submit one copy to Kluwer