Innovative hybrid approaches to the processing of textual data April 22, 2012, Avignon, France
** Extended deadline: Feb 05, 2012 **
Submission Deadline: Jan 27, 2012
The hybrid approach term covers a large set of situations in which different approaches are combined in order to better process textual data and to attempt a better achievement of the dedicated task.
Among the hybridizations the possible combinations are unlimited. The most frequent combination, as stressed during The Balancing Act in 1994, addressed machine learning and rule-based systems. Beyond this, the hybridization can be augmented with distributionnal approaches, syntactic and morphological analyses, semantic distances and similarities, graph theory models, cooccurrences of linguistic units (e.g., word and their dependencies, word senses and pos-tag, NEs and semantic roles,...), knowledge-based approaches (terminologies and ontologies), etc.
As a matter of fact, the hybridization implies to define a strategy to efficiently combine several approaches: cooperation between approaches, filtering, voting or ranking of the multiple system outputs, etc.
Indeed, the combination of these different methods and approaches appears to provide more complete and performant results. The reason is that each method is sensitive and efficient with given data and within given contexts. Hence, their combination may improve both precision and recall. The coverage is indeed improved, while the exploitation of different methods may also lead to the improvement of the precision since their use within filtering, voting etc. modes becomes possible.
In this workshop, we favour the extended meaning of the hybridization of methods, applied to various application areas, such as (but do not feel constrained by these):
- automatic creation of linguistic resources - POS tagging - building and structuring of terminologies - information retrieval and filtering - information extraction - linguistic annotation - semantic labeling - sign language recognition and transcription - oral data transcription - filtering and validation of lexical resources - text summarization - question/answering system - natural language generation - etc.
We invite authors to submit novel methods and novel conceptions of the hybridization performed in various areas related to the textual data processing.
Nov 25, 2011: 1st workshop CFP Jan 04, 2012: Abstract deadline (optional) Feb 05, 2012: Paper due date (Extended deadline) Feb 29, 2012: Notification of acceptance Mar 09, 2012: Camera-ready deadline Apr 22, 2012: Workshop
Authors are invited to submit full papers on original, unpublished work in the topic area of this workshop. Submissions should be formatted using the EACL 2012 stylefiles for latex or MS Word, with blind review and not exceeding 8 pages plus an extra page for references. The PDF files will be submitted electronically at https://www.softconf.com/eacl2012/Hybrid2012/
Delphine Bernhard, LiLPa, Université de Strasbourg, France Philipp Cimiano, CITEC, University of Bielefeld, Germany Vincent Claveau, IRISA-CNRS, Rennes, France Kevin Cohen, University of Colorado Health Sciences Center, USA Marie-Claude l'Homme, OLST, Université de Montreal, Canada Béatrice Daille, Université de Nantes, LINA, France Stefan Th. Gries, University of California, Santa Barbara, USA Anna Kazantseva, University of Ottawa, Canada Alistair Kennedy, University of Ottawa, Canada Ben Leong, University of North Texas, USA Bruno Pouliquen, WIPO, Geneva, Switzerland Sampo Pyysalo, National Centre for Text Mining, University of Manchester, United Kingdom Mathieu Roche, LIRMM, Université de Montpellier 2, France Patrick Ruch, Haute école de gestion de Genčve, Switzerland Paul Thompson, National Centre for Text Mining, University of Manchester, United Kingdom Özlem Uzuner, University at Albany, State University of New York, USA
Natalia Grabar, CNRS UMR 8163 STL, Université Lille 1&3, France Marie Dupuch, MOSTRARE/LIFL & CNRS UMR 8163 STL, Université Lille 1&3, France Amandine Périnet, LIM&BIO, Université Paris 13, France Thierry Hamon, LIM&BIO, Université Paris 13, France