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.
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:
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:
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:
|Call for papers:||30 November 2010|
|Submission of articles:||20 April 2011|
|Preliminary decisions to authors:||26 July 2011|
|Submission of revised articles:||28 Septmber 2011|
|Final decisions to authors:||8 December 2011|
|Final versions due from authors:||27 December 2011|
TALP Research Center, Technical University of Catalonia
lluism a t lsi d o t upc d o t edu
Information Engineering and Computer Science Department, University of Trento
moschitti a t dit d o t unitn d o t it
Roberto Basili, University of Rome, Italy
Ulf Brefeld, Yahoo-Reseatch, Spain
Razvan Bunescu, Ohio University, US
Nicola Cancedda, Xerox, France
Stephen Clark, University of Cambridge, UK
Trevor Cohn, University of Sheffield, UK
Xavier Carreras, Universitat Politècnica de Catalunya, Spain
Walter Daelemans, University of Antwerp, Belgium
Jason Eisner, John Hopkins University, US
Hal Daumé, University of Maryland, US
James Henderson, University of Geneva, Switzerland
Liang Huang, ISI, University of Southern California, US
Richard Johansson, University of Gothenburg, Sweden
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
Raymon Money, 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 Yih, Microsoft Research, US
Fabio Massimo Zanzotto, University of Rome, Italy
Min Zhang, A-STAR, Singapore
Articles submitted to this special issue must adhere to the NLE
(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 JNLE-SIO@disi.unitn.it
- with subject filed: JNLE-SIO and
- including names of the authors and title of the submission in the body
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 website:
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 of JNLE-SIO.
Last Update: March 12, 2011