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JNLE-SIO 2012 : Special Issue for the Journal of Natural Language Engineering on Statistical Learning of Natural Language Structured Input and Output


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Submission Deadline Mar 20, 2011
Notification Due Jun 26, 2011
Final Version Due Nov 27, 2011
Categories    NLP

Call For Papers

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 Output

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 constituents.

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 deep.

* 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 architectures
* 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 data

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:
(refers to the 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 body

Guest editors
Lluís Màrquez
TALP Research Center, Technical University of Catalonia

Alessandro Moschitti
Information Engineering and Computer Science Department, University of

Guest editorial bord (preliminary)
Roberto Basili, University of Rome, Italy
Razvan Bunescu, Ohio University, US
Stephen Clark, University of Cambridge, UK
Nicola Cancedda, Xerox, France
Trevor Cohn, University of Sheffield, UK
Walter Daelemans, University of Antwerp, Belgium
Liang Huang, ISI, University of Southern California, US
Mirella Lapata, University of Edinburgh, UK
Percy Liang, University of Berkeley, US
Yuji Matsumoto, Nara Institute of Science and Technology, Japan
Ryan McDonald, Microsoft Research, US
Hwee Tou Ng, National University of 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
Jun'ichi Tsujii, University of Tokyo, Japan
Antal van den Bosch, Tilburg University, The Netherlands
Scott Yih, Microsoft Research, US
Fabio Massimo Zanzotto, University of Rome, Italy
Min Zhang, A-STAR, Singapore

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