Appel: Special issue of JNLE, Statistical Learning of Natural Language Structured Input and Output

Thierry Hamon thierry.hamon at UNIV-PARIS13.FR
Tue Feb 15 19:41:26 UTC 2011

Date: Sat, 12 Feb 2011 11:02:00 -0700
From: Alessandro Moschitti <moschitti at>
Message-Id: <3B7F0420-7680-4B78-8442-3C0D5F31A812 at>

                   C A L L     f o r    P A P E R S

          Special Issue for the Journal of Natural Language
                            Engineering on
    Statistical Learning of Natural Language Structured Input and
(Apologies for multiple postings)

Machine learning and statistical approaches have become indispensable
for large part of Computational Linguistics and Natural Language
Processing research. On one hand, they have enhanced systems' accuracy
and have significantly sped-up some design phases, e.g. the inference
phase. On the other hand, their use requires careful parameter tuning
and, above all, engineering of machine-based representations of
natural language phenomena, e.g. by means of features, which sometimes
detach from the common sense interpretation of such phenomena.

These difficulties become more marked when the input/output data have
a structured and relational form: the designer has both to engineer
features for representing the system input, e.g. the syntactic parse
tree of a sentence, and devise methods for generating the output,
e.g. by building a set of classifiers, which provide boundaries and
type (argument, function or concept type) of some of the parse-tree

Research in empirical Natural Language Processing has been tackling
these complexities since the early work in the field,
e.g. part-of-speech tagging is a problem in which the input --word
sequences-- and output --POS-tag sequences-- are structured.  However,
the models initially designed were mainly based on local
information. The use of such ad hoc solutions was mainly due to the
lack of statistical and machine learning theory suggesting how models
should be designed and trained for capturing dependencies among the
items in the input/output structured data. In contrast, recent work in
machine learning has provided several paradigms to globally represent
and process such data: structural kernel methods, linear models for
structure learning, graphical models, constrained conditional models,
and re-ranking, among others.

However, none of the above approaches has been shown to be superior in
general to the rest. A general expressivity-efficiency trade off is
observed, making the best option usually task-dependant. Overall, the
special issue is devoted to study engineering techniques for
effectively using natural language structures in the input and in the
output of typical computational linguistics applications. Therefore,
the study on generalization of new or traditional methods, which allow
for fast design in different or novel NLP tasks is one important aim
of this special issue.

Finally, the special issue is also seeking for (partial) answers to
the following questions:

   * Is there any evidence (empirical or theoretical) that can
     establish the superiority of one class of learning
     algorithms/paradigms over the others when applied to some
     concrete natural language structures?

   * When we use different classes of methods, e.g. SVMs vs CRFs, or
     different paradigms, what do we loose and what do we gain from a
     practical viewpoint (implementation, efficiency and accuracy)?
     This question is particularly interesting, when considering
     different structure types: syntactic or semantic both shallow or

   * Can we empirically demonstrate that theoretically motivated
     algorithms, e.g. SVM-struct, improve simpler models,
     e.g. re-ranking, in the NLP case?

   * Are there any other novel engineering approaches to NLP input and
     output structures?


For this special issue we invite submissions of papers describing
novel and challenging work/results in theories, models, applications
or empirical studies on statistical learning for natural language
processing involving structured input and/or structured output.
Therefore, the invited submission must concern with (a) any kind of
natural language problems; and (b) natural language structured data.

Assuming the target above, the range of topics to be covered will
include, but will not be limited to the following:

   * Practical and theoretical new learning approaches and
   * Experimental evaluation/comparison of different approaches
   * Kernel Methods
   * Algorithms for structure output (batch and on–line):
     – structured SVMs, Perceptron, etc.
     – on sequences, trees, graphs, etc.
   * Bayesian Learning, Generative Models, Graphical Models
   * Relational Learning
   * Constraint Conditional models
   * Integer Linear Programming approaches
   * Graph-based algorithms
   * Ranking and Reranking
   * Scalability and effciency of ML methods
   * Robust approaches
     – noisy data, domain adaptation, small training sets, etc.
   * Unsupervised and semi-supervised models
   * Encoding of syntactic/semantic structures
   * Structured data encoding deep semantic information and relations
   * Relation between the syntactic and semantic layers in structured


Call for papers: 	  		        30 November 2010
Submission of articles: 	  	        20 March 2011
Preliminary decisions to authors: 	26 June 2011
Submission of revised articles: 	28 August 2011
Final decisions to authors: 	  	23 October 2011
Final versions due from authors: 	27 November 2011


Articles submitted to this special issue must adhere to the NLE
journal guidelines available at:

(see section "Manuscript requirements" for the journal latex style).

We encourage authors to keep their submissions below 30 pages.
Send your manuscript in pdf attached to an email addressed to
   - with subject filed: JNLE-SIO and
   - including names of the authors and title of the submission in the

An alternative way to submit to JNLE-SIO is to submit a paper to
TextGraph 6 and being selected for contributing to JNLE. See the

The selected workshop papers must be extended to journal papers by
following the indications of both the TextGraph 6 reviewers and the
JNLE-SIO editors. These upgraded versions have to be submitted to
JNLE-SIO no later than August 28, 2011 for the second round of review


Lluís Màrquez
TALP Research Center, Technical University of Catalonia
lluism at

Alessandro Moschitti
Information Engineering and Computer Science Department, University of
moschitti at


Roberto Basili, University of Rome, Italy
Ulf Brefeld, Yahoo!-Research, Spain
Razvan Bunescu, Ohio University, US
Nicola Cancedda, Xerox, France
Xavier Carreras, UPC, Spain
Stephen Clark, University of Cambridge, UK
Trevor Cohn, University of Sheffield, UK
Walter Daelemans, University of Antwerp, Belgium
Hal Daumé, University of Maryland, US
Jason Eisner, John Hopkins University, US
James Henderson, University of Geneva, Switzerland
Liang Huang, ISI, University of Southern California, US
Terry Koo, MIT CSAIL, US
Mirella Lapata, University of Edinburgh, UK
Yuji Matsumoto, Nara Institute of Science and Technology, Japan
Ryan McDonald, Microsoft Research, US
Raymond Mooney, University of Texas at Austin, US	
Hwee Tou Ng, National University of Singapore, Singapore
Sebastian Riedel, University of Massachusetts, US
Dan Roth, University of Illinois at Urbana Champaign, US
Mihai Surdeanu, Stanford University, US
Ivan Titov, Saarland University, Germany
Kristina Toutanova, Microsoft Research, US
Jun'ichi Tsujii, University of Tokyo, Japan
Antal van den Bosch, Tilburg University, The Netherlands
Scott Wen-tau Yih, Microsoft Research, US
Fabio Massimo Zanzotto, University of Rome "Tor Vergata", Italy
Min Zhang, A-STAR, Singapore

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