[Corpora-List] Learning Language in Logic (LLL05): 1st CFP

From: James Cussens (jc@cs.york.ac.uk)
Date: Fri Feb 11 2005 - 17:11:54 MET

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                            FIRST CALL FOR PAPERS

                        The 4th International Workshop
                                      on
                      Learning Language in Logic (LLL05)
                   http://www.cs.york.ac.uk/aig/lll/lll05/

           Special Theme: Extracting Relations from Bio-medical Texts
                http://genome.jouy.inra.fr/texte/LLLchallenge

                               Bonn, Germany
                               7 August 2005

                             Hosted by ICML 2005,
            The 22nd International Conference on Machine Learning
                      http://icml2005.kdnet.org/icml.php

    INTRODUCTION

    This workshop is the fourth Learning Language in Logic (LLL) workshop
    following previous LLL workshops in Bled (99), Lisbon (00) and
    Strasbourg (01). The purpose of the workshop is to provide a focus for
    work which applies machine learning using logical representations to
    natural language. Much existing work in this area uses techniques from
    inductive logic programming (ILP), and increasingly statistical
    relational learning. More details on the LLL state-of-the-art are
    available from the LLL home page ( http://www.cs.york.ac.uk/aig/lll/ )
    which includes links to many LLL papers, including introductory ones.
    We encourage submission of papers on any aspect of logic-based machine
    learning for natural language learning.

    One of the principal motivations for proposing such a workshop now is
    the increasing interest in applying logic-based machine learning to
    the problem of Information Extraction from bio-medical texts. For this
    reason the workshop has an associated "challenge task" where the aim
    is to learn IE rules which can be used to extract information on
    gene/protein interactions. Information on the challenge task is given
    below. Although, bio-medical IE is a special theme of the workshop, we
    very much encourage good quality articles on any aspect of logic-based
    machine learning for natural language learning. Here is a
    non-exhaustive list of possible topics:

      * Explanation-based learning
      * Extracting predicate-argument structure
      * Grammar learning
      * Higher-order logic for LLL
      * Handling very complex terms in ILP
      * Information extraction
      * Instance-based approaches in ILP
      * Logical approaches to statistical NLP
      * Learning subcategorisation frames
      * Morphological analysis
      * Multi-predicate learning
      * POS and morphosyntactic tagging
      * Semantic role labelling
      * Shallow parsing
      * Theory revision
      * Using ILP with large data sets
      * Word sense disambiguation

    ----------------------------------------------------------------------

    CHALLENGE TASK
    http://genome.jouy.inra.fr/texte/LLLchallenge

    The LLL05 challenge task is to learn rules to extract gene/protein
    interactions from bio-medical abstracts. The challenge task team at
    MIG-INRA are providing training data containing the following information:

    - annotation indicating agent and target of an interaction
    - dictionary of named entities (including variants and synonyms)
    - linguistic information: word segmentation, lemmatisation, syntactic
       categories and syntactic dependencies

    An initial version of the data, containing only agent/target
    annotation has been released. This data and full details of the LLL05
    bio-medical challenge task are available from the challenge task
    web page ( http://genome.jouy.inra.fr/texte/LLLchallenge ).

    Results for this task are to be reported by the submission of a 4 page
    paper.

    Challenge task schedule:

    11 February 2005 Initial version of the data made available
      3 March 2005 Final version of the data made available
    27 April 2005 Submission of papers with results
    11 May 2005 Challenge task papers notification

    ----------------------------------------------------------------------

    SUBMISSION DETAILS:

    Normal paper format: 8 pages
    Challenge task paper format: 4 pages

    The actual mechanics of submitting your paper are still being
    sorted. Once decided this information will be available of the main
    workshop web page.

    ----------------------------------------------------------------------

    ORGANISERS:

    James Cussens, University of York, UK (workshop)
    Claire Nédellec, INRA, France (challenge task)

    PROGRAMME COMMITTEE:

         * Zoltán Alexin, University of Szeged, Hungary
         * Érick Alphonse, INRA, France
         * Mary Elaine Califf, Illinois State University, USA
         * Vincent Claveau, Université de Montréal, Canada
         * James Cussens, University of York, UK
         * Walter Daelemans, University of Antwerp, Belgium
         * Saso Dzeroski, Jozef Stefan Institute, Slovenia
         * Tomaz Erjavec, Jozef Stefan Institute, Slovenia
         * Dimitar Kazakov, University of York, UK
         * Suresh Manandhar, University of York, UK
         * Claire Nédellec, INRA, France
         * Lubos Popelínský, Masaryk University, Czech Republic
         * Stephen Pulman, University of Oxford, UK
         * Dan Roth, University of Illinois at Urbana-Champaign, USA
         * Pascale Sébillot, IRISA, France
         * Jude Shavlik, University of Wisconsin-Madison, USA

    ----------------------------------------------------------------------

    IMPORTANT DATES:

    11 February 2005 Initial version of the challenge task data available
      3 March 2005 Final version of the challenge task data available
      1 April 2005 Normal paper submission deadline
    22 April 2005 Normal paper notification
    27 April 2005 Challenge task paper submission deadline
    11 May 2005 Challenge task paper notification
    13 May 2005 Normal paper (final version) deadline
    30 May 2005 Workshop notes available on-line

    ----------------------------------------------------------------------



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