(Submission deadline extended to October 8, 2003)
Machine Learning Journal
Special Issue on Learning in Speech and Language Technologies
Call for Papers
http://www.ee.ust.hk/~pascale/MLJspecial.html
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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@ee.ust.hk) (University of Science & Technology, HKUST)
Dan Roth (danr@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 (IBM T.J. Watson Research Center)
Richard Schwartz (BBN Technologies)
Richard Sproat (University of Illinois at Urbana/Champaign)
Dekai Wu (University of Science & Technology, HKUST)
Schedule:
October 8, 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 not exceed 8000 words and should conform to the
formatting instructions in:
http://www.cs.ualberta.ca/~holte/mlj/info-for-authors.html
(2) Electronic submission:
Submission instructions are available here.
However, in addition to everything stated there, for papers submitted to
special issues:
1. send an email with title page to pascale@ee.ust.hk with paper title and
author information. The first author will be the primary contact unless
otherwise stated.
2. state clearly in the body of the email submission that it is for THIS
special issue
3. copy all submissions to Kluwer to danr@cs.uiuc.edu
4. please make sure you submit one copy to Kluwer
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