Appel: special issue of TAL on Language Models (deadline extension)

[Thierry Hamon]

Date: Tue, 8 Oct 2002 18:20:18 +0200
From: Jardino <Michele.Jardino at limsi.fr>
Message-Id: <200210081619.RAA05861 at m7.limsi.fr>
X-url: http://www.hermes-science.com/


Dear colleague
Could you give a large diffusion to the deadline extension (Mon, 14 October)
of the Call for Papers, concerning a special issue of the TAL journal on
Language Models. (see below)

Thanks in advance
--
Best Regards, Michele Jardino

************************************************************************
Second call for papers (TAL journal ): Deadline Extension to October
14, 2002



                    Automated Learning of Language Models



                          Deadline for submission :

                             October 14, 2002

            Issue coordinated by Michèle Jardino (CNRS, LIMSI),
             and Marc El-Beze    (LIA, University of Avignon) .

---

Language Models (LM) play a crucial role in the working of Automated
Natural Language Processing systems, when real-life problems (often
very large ones) are being dealt with. Instances are Speech
Recognition, Machine Translation and Information Retrieval. If we want
these systems to adapt to new applications, or to follow the evolution
in user behaviour, we need to automatize the learning of parameters in
the models we use. Adaptation should occur in advance or in real
time. Some applications do not allow us to build an adequate corpus,
either from a quantitative or qualitative point of view. The gathering
of learning data is made easier by the richness of Web resources, but
in that huge mass, we have to effectively separate the wheat from the
chaff.


When asked about the optimal size for a learning corpus, are we
satisfied to answer "The bigger, the better"?

Rather than training one LM on a gigantic learning corpus, would it
not be advisable to fragment this corpus into linguistically coherent
segments, and learn several language models, whose scores might be
combined when doing the test (model mixture)?

In the case of n-gram models, which is the optimal value for n? Should
it be fixed or variable?

A larger value allows us to capture linguistic constraints over a
context which goes beyond the mere two preceding words of the classic
trigram.  However, increasing n threatens us with serious coverage
problems. Which is the best trade-off between these two opposite
constraints?  How can we smooth models in order to approximate
phenomena that have not been learned? Which alternatives are to be
chosen, using which more general information (lesser-order n-grams,
n-classes?)

Beyond the traditional opposition between numerical and
knowledge-based approaches, there is a consensus about the
introduction of rules into stochastic models, or probability into
grammars, hoping to get the best of both strategies. Hybrid models can
be conceived in several ways, depending on which choices are made
regarding both of their sides, and also, the place where coupling
occurs. Because of discrepancies between the language a grammar
generates, and actually observed syntagms, some researchers decided to
reverse the situation and derive the grammar from observed facts.
However, this method yields disappointing results, since it does not
perform any better than n -gram methods, and is perhaps
inferior. Shouldn't we introduce here a good deal of supervision, if
we want to reach this goal?

Topics (non-exhaustive list)

------------------------------------------------------------------------

In this special issue, we would like to publish either innovative
papers, or surveys and prospective essays dealing with Language Models
(LM), Automated Learning of their parameters, and covering one of
following subtopics:

   * Language Models and Resources:
        o determination of the adequate lexicon
        o determination of the adequate corpus
   * Topical Models
   * LM with fixed or variable history
   * Probabilistic Grammars
   * Grammatical Inference
   * Hybrid Language Models
   * Static and dynamic adaptation of LMs
   * Dealing with the Unknown
        o Modelling words which do not belong to the vocabulary
        o Methods for smoothing LMs
   * Supervised and unsupervised learning of LMs
        o Automated classification of basic units
        o Introducing linguistic knowledge into LMs
   * Methods for LM learning
        o EM, MMI, others?
   * Evaluation of Language Models
   * Complexity and LM theory

   * Applications:
     - Speech Recognition
     - Machine Translation
     - Information Retrieval

Format

------------------------------------------------------------------------

Papers (25 pages maximum) are to be submitted in Word ou LaTeX. Style
sheets are available at HERMES :
< http://www.hermes-science.com/ >.

Language

  ------------------------------------------------------------------------

Articles can be written either in French or in English, but English
will be accepted from non-French speaking authors only.

Deadlines

  ------------------------------------------------------------------------

Submission deadline is October 7, 2002.

Articles will be reviewed by a member of the editorial board and two
external reviewers designed by the editors of this issue. Decisions of
the editorial board and referees' report will be transmitted to the
authors before November 20, 2002.

The final version of the accepted papers will be required by February
20, 2003. Publication is planned during the spring of 2003.

Submission

  ------------------------------------------------------------------------

Submissions must be sent electronically to:

tal.ml at limsi.fr)

alias for e-mail adresses of Michele Jardino ( jardino at limsi.fr) and
Marc El-Beze ( marc.elbeze at lia.univ-avignon.fr),


or, in paper version (four copies), posted to:

Marc El-Beze Laboratoire d'Informatique
LIA - CERI BP 1228
84 911 AVIGNON CEDEX 9 FRANCE

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