MMIES2 2008

MMIES2 2008
Multi-source, Multilingual Information Extraction and Summarization Workshop
Dates	Aug 23, 2008 (iCal) - Aug 23, 2008
Homepage:	doremi.cs.helsinki.fi/mmies2
Location
Location:	Manchester, UK
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Important dates
Submissions:	May 5, 2008
Notification:	Jun 6, 2008
Table of Contents

MMIES2: Multi-source, Multilingual Information Extraction and
Summarization Workshop

Manchester, 23 August, 2008

Held in conjunction with COLING-2008 (http://www.coling2008.org.uk/),
the 22nd International Conference on Computational Linguistics, 18-22
August,  2008.


Theme:

The objective  of the  2nd MMIES Workshop:  Multi-source, Multilingual
Information  Extraction   and  Summarization  is   to  bring  together
researchers   and   practitioners   in   the  areas   of   extraction,
summarization, and  other information access  technologies, to discuss
recent  approaches  to   multi-source  and  multi-lingual  challenges.
Approaches  to handling  the idiosyncratic  nature of  the  new Web2.0
media  are especially  welcome,  including: mixed  input, new  jargon,
ungrammatical and mixed-language input, and emotional discourse.


Workshop Web Site:

http://doremi.cs.helsinki.fi/mmies2/



Organisers:

* Sivaji Bandyopadhyay (Jadavpur University, India)
* Thierry Poibeau (CNRS / Universite Paris 13, France)
* Horacio Saggion (University of Sheffield, UK)
* Roman Yangarber (University of Helsinki, Finland)


Call for Papers

Information  extraction  (IE)  and  text summarization  (TS)  are  key
technologies  aiming  at extracting  from  texts  information that  is
relevant  to a  user's  interest, and  presenting  it to  the user  in
concise  form.  The  on-going information  explosion makes  IE  and TS
particularly   critical   for   successful  functioning   within   the
information society.  These technologies, however, face new challenges
with the adoption of the  Web 2.0 paradigm (e.g. blogs, wikis) because
of  their inherent  multi-source nature.   These technologies  must no
longer only  deal with isolated  texts or single narratives,  but with
large-scale repositories  or sources -- possibly  in several languages
-- containing a  multiplicity of  views, opinions, or  commentaries on
particular topics, entities or events.   There is thus a need to adapt
and/or develop new techniques to deal with these new phenomena.


Recognising  similar information  across different  sources  and/or in
different languages  is of paramount importance  in this multi-source,
multi-lingual context.  In information extraction, merging information
from multiple sources can lead  to increased accuracy as compared with
extraction from a single  source. In text summarization, similar facts
found  across  sources can  inform  sentence  scoring algorithms.   In
question answering,  the distribution  of answers in  similar contexts
can inform answer ranking components.


Often, it is  not the similarity of information  that matters, but its
complementary   nature.  In   a  multi-lingual   context,  information
extraction   and  text   summarization  can   provide   solutions  for
cross-lingual access: key pieces  of information can be extracted from
different texts in one or many languages, merged, and then conveyed in
many natural languages in concise  form.  Applications need to be able
to cope with the idiosyncratic nature  of the new Web 2.0 media: mixed
input, new  jargon, ungrammatical and  mixed-language input, emotional
discourse, etc.   In this context, synthesizing  or inferring opinions
from multiple  sources is  a new and  exciting challenge for  NLP.  On
another level, profiling  of individuals who engage in  the new social
Web,    and   identifying    whether   a    particular    opinion   is
appropriate/relevant  in a given  context are  important topics  to be
addressed.


It is therefore important that the research community address the following
issues:

- What methods are appropriate to detect similar/complementary/contradictory
information? Are hand-crafted rules and knowledge-rich approaches
convenient?

- What methods are available to tackle cross-document and cross-lingual
entity and event coreference?

- What machine learning approaches are most appropriate for this task --
supervised/unsupervised/semi-supervised?  What type of corpora are
required for
training and testing?

- What techniques are appropriate to synthesize condensed synopses of the
extracted information?  What generation techniques are useful here? What
kind
of techniques can be used to cross domains and languages?

- What techniques can improve opinion mining and sentiment analysis through
multi-document analysis?  How do information extraction and opinion mining
connect?

- What tools exist for supporting multi-lingual/multi-source access to
information?  What solutions exist beyond full document translation to
produce
cross-lingual summaries?



Important Dates:

* Paper submission deadline: ***  5 May ***
* Notification of acceptance of Papers: 6 June
* Camera-ready copy of papers due: 1 July
* Workshop: *** 23 August ***


Paper Submission:


Papers should describe original work  and should indicate the state of
completion  of the  reported results.  Wherever  appropriate, concrete
evaluation results  should be included. Submissions will  be judged on
correctness,   originality,  technical   strength,   significance  and
relevance to the conference, and interest to the attendees.

Submissions should follow the two-column format of ACL proceedings and
should not  exceed eight (8) pages, including  references. We strongly
recommend the  use of the Coling  2008 LaTeX style  files or Microsoft
Word    Style   files    tailored   for    this    year's   conference
(http://personalpages.manchester.ac.uk/staff/harold.somers/coling/style.html).

Submission will be electronic (pdf format only), using the START paper
submission      webpage       dedicated      to      the      workshop
https://www.softconf.com/coling08/MMIES2/.

The  reviewing process  will  be  blind and  each  submission will  be
reviewed by at least three programme committee members.



Programme Committee:

 Javier Artiles (UNED, Spain)
 Kalina Bontcheva (U. Sheffield, UK)
 Nathalie Colineau (CSIRO, Australia)
 Nigel Collier (NII, Japan)
 Hercules Dalianis (KTH/Stockholm University, Sweden)
 Thierry Declerk (DFKI, Germany)
 Michel Généreux (LIPN-CNRS, France)
 Julio Gonzalo (UNED, Spain)
 Brigitte Grau (LIMSI-CNRS, France)
 Ralph Grishman (New York University, USA)
 Kentaro Inui (NAIST, Japan)
 Min-Yen Kan (National University of Singapore, Singapore)
 Guy Lapalme (U. Montreal, Canada)
 Diana Maynard (U. Sheffield, UK)
 Jean-Luc Minel (Modyco-CNRS, France)
 Constantin Orasan (University of Wolverhampton, UK)
 Cecile Paris (CSIRO, Australia)
 Maria Teresa Pazienza (U. of Roma tor Vergata, Italy)
 Bruno Pouliquen (European Commission - Joint Research Centre, Italy)
 Satoshi Sekine (NYU, USA)
 Patrick Saint-Dizier (IRIT-CNRS, France)
 Agnes Sandor (Xerox XRCE, France)
 Ralf Steinberger (European Commission - Joint Research Centre, Italy)
 Stan Szpakowicz (University of Ottawa, Canada)
 Lucy Vanderwende (Microsoft Research, USA)
 Jose Luis Vicedo (Universidad de Alicante, Spain)


Additional Information:

Information about the previous MMIES Workshop, at  RANLP-2007 in
Borovets,  Bulgaria  can be found at 
(http://www-lipn.univ-paris13.fr/~poibeau/mmies/index.html
	

This CfP was obtained from WikiCFP