[Elsnet-list] Deadline Extended: HLIE08 - Workshop on High-level Information Extraction ECML PKDD 2008

Sebastian Blohm blohm at aifb.uni-karlsruhe.de
Tue Jun 17 18:50:02 CEST 2008

                            Call for Papers
          HLIE08: Workshop on High-level Information Extraction
                         at ECML PKDD 2008


                    Antwerp, September 19th, 2008

The ECML-08 Workshop on High-level Information Extraction will be held 
in conjunction with the European Conference on Machine Learning and 
Principles and Practice of Knowledge Discovery in Databases that will 
take place September 15 to 19, 2008, in Antwerp, Belgium.


+++ The deadline was extended by two weeks to June 30th +++


Information extraction (IE) techniques aim to extract informations from 
data sources. IE methods versatilely address naturally arising learning 
tasks where the data is generally structured, highly correlated, and 
frequently preserve multiple-way dependencies within and between 
recurrent structures. By now, "low-level" tasks such as named entity 
recognition are well understood, however, solving complex IE tasks -- 
like relation and event-extraction -- remains a challenge.

In the last years, significant contributions to high-level IE in 
relevant fields led to applications that have matured to a point beyond 
proof of concept. However, which strategy (e.g., pipeline, structured, 
or hybrid) is beneficial for which problems is not yet well understood, 
neither from the theoretical nor the practical point of view.

We aim at bringing together an interdisciplinary group of researchers 
who are working on high-level information extraction. The goal of this 
workshop will be to structure and explore the state of the art, to 
evolve high-level IE models with regard to real-world applications, and 
to identify future challenges and applications. We intend to cover a 
broad range of methods, including pipelined/hybrid approaches and 
structured prediction models; in particular we are interested in the 
following topics:

# Algorithms:
What are the differences between pipelined and structured methods? Are 
there hybrid methods, using the best of the two worlds? Are there novel 
algorithms and techniques for solving high-level IE or subproblems thereof?

# Theoretical results:
Are there convergence/generalization bounds for high-level IE 
techniques? Is there a characterization of problems for which a direct 
solution always exists? How can high-level IE methods be evaluated?

# Pre- and post-processing techniques:
Which high-level IE applications benefit from pre-/post-processing? Can 
pre-/post-processing be harmful? Are these techniques independent of the 
underlying IE methods? How can pre- and post-processing techniques be 

# Applications:
What are novel applications involving high-level IE? Are there 
equivalent problems in related areas that can be solved with existing 

For further details and topics of interest, please refer to the website 
of the workshop.


Paper Submission deadline :    June 30th
Notification of acceptance:    July 16th (tentative)
Final paper deadline      :    August 7th
Workshop                  :    September 19th


We strongly encourage researchers in the fields of machine learning, 
statistics, information retrieval, natural language processing, 
computational biology, computer vision, and robotics to submit their 
work. Authors instructions and style files can be downloaded at 
http://www.springer.de/comp/lncs/authors.html. The maximum length for 
submissions is 6 pages for abstracts or 12 pages for full papers in this 

Papers submitted to HLIE 2008 will normally be reviewed by two referees.


* Sebastian Blohm (University of Karlsruhe)
* Ulf Brefeld (TU Berlin)
* Felix Jungermann (University of Dortmund)
* Roman Yangarber (University of Helsinki)


* Workshop web page:   http://www-ai.cs.tu-dortmund.de/HLIE08/index.html
* Conference web page: http://www.ecmlpkdd2008.org/


Felix [dot] Jungermann [at] cs [dot] tu-dortmund [dot] de
Sebastian [dot] Blohm [at] aifb [dot] uni-karlsruhe [dot] de


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