Learning Language in Logic (LLL05)

Challenge task: Extracting Relations from Bio-medical Texts

ICML05 workshop, 7 August 2005, Bonn, Germany

Endorsed by FoLLI

[Proceedings] [Workshop schedule] [Call for papers (now defunct)] [Challenge Task] [Organisers] [Programme Committee] [Previous LLL workshops]

Proceedings

The workshop proceedings are now available as a single PDF file.

Workshop schedule

Call for papers

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, 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. 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
• Morphosyntactic tagging
• Multi-predicate learning
• Part-of-speech tagging
• Shallow parsing
• Theory revision
• Using ILP with large data sets

Challenge task

Full details of the LLL05 bio-medical challenge task are available from INRA's challenge task 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
• Sašo Džeroski, Jožef Stefan Institute, Slovenia
• Tomaž Erjavec, Jožef Stefan Institute, Slovenia
• Dimitar Kazakov, University of York, UK
• Suresh Manandhar, University of York, UK
• Claire Nédellec, INRA, France
• Luboš 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

Previous LLL workshops

• LLL99, Bled, Slovenia
• LLL00, Lisbon, Portugal
• LLL01, Strasbourg, France