June 4 or 5, 2009, Boulder, Colorado, USA http://www-tsujii.is.s.u-tokyo.ac.jp/ilpnlp/
Call for Papers (Submission deadline: March 6, 2009) ========================================================== Integer Linear Programming (ILP) has recently attracted much attention within the NLP community. Formulating problems using ILP has several advantages. It allows us to focus on the modelling of problems, rather than engineering new search algorithms; provides the opportunity to incorporate generic global constraints; and guarantees exact inference. This and the availability of off-the-shelf solvers has lead to a large variety of natural language processing tasks being formulated in the ILP framework, including semantic role labelling, syntactic parsing, summarisation and joint information extraction.
The use of ILP brings many benefits and opportunities but there are still challenges for the community; these include: formulations of new applications, dealing with large-scale problems and understanding the interaction between learning and inference at training and decision time. The purpose of this workshop is to bring together researchers interested in exploiting ILP for NLP applications and tackling the issues involved. We are interested in a broad range of topics including, but not limited to:
- Novel ILP formulations of NLP tasks. This includes: the introduction of ILP formulations of tasks yet to be tackled within the framework; and novel formulations, such as equivalent LP relaxations, that are more efficient to process than previous formulations.
- Learning and Inference. This includes issues relating to: decoupling of learning (e.g., learning through local classifiers) and inference, learning with exact (e.g., ILP) or approximate inference, learning of constraints, learning weights for soft constraints, and the impact of ignoring various constraints during learning.
- The utility of global hard and soft constraints in NLP. Sometimes constraints do not increase accuracy (and can even decrease it), when and why do global constraints become useful? For example, do global constraints become more important if we have less data?
- Formulating and solving large NLP problems. Applying ILP to hard problems (such as parsing, machine translation and solving several NLP tasks at once) often results in very large formulations which can be impossible to solve directly by the ILP engine. This may require exploring different ILP solving methods (such as, approximate ILP solvers/methods) or cutting plane and pricing techniques.
- Alternative declarative approaches. A variety of other modeling frameworks exist, of which ILP is just one instance. Using other approaches, such as weighted MAX-SAT, Constraint Satisfaction Problems (CSP) or Markov Networks, could be more suitable than ILP in some cases. It can also be helpful to model a problem in one framework (e.g., Markov Networks) and solve them with another (e.g., ILP) by using general mappings between representations.
- First Order Modelling Languages. ILP, and other essentially propositional languages, require the creation of wrapper code to generate an ILP formulation for each problem instance. First (Higher) Order languages, such as Learning Based Java and Markov Logic, reduce this overhead and can also aid the solver to be more efficient. Moreover, with such languages the automatic exploration of the model space is easier.
We encourage submissions addressing the above questions and topics or other relevant issues. Papers are limited to 8 pages; the corresponding style files will be made available soon. Note that reviewing will be blind and hence no author information should be included in the papers.
IMPORTANT DATES: March 6, 2009: Submission deadline March 30, 2009: Notification of acceptance April 12, 2009: Camera-ready copies due June 4 or 5, 2009: Workshop held in conjunction with NAACL HLT (exact date to be announced)
INVITED SPEAKER: Dan Roth (University of Illinois at Urbana-Champaign)
PROGRAM COMMITTEE: - Dan Roth (University of Illinois at Urbana-Champaign) - Mirella Lapata (University of Edinburgh) - Scott Yih (Microsoft Research) - Nick Rizzolo (University of Illinois at Urbana-Champaign) - Ming-Wei Chang (University of Illinois at Urbana-Champaign) - Ivan Meza-Ruiz (University of Edinburgh) - Ryan McDonald (Google Research) - Jenny Rose Finkel (Stanford University) - Pascal Denis (INRIA Paris-Rocquencourt) - Manfred Klenner (University of Zurich) - Hal Daume III (University of Utah) - Daniel Marcu (University of Southern California) - Kevin Knight (University of Southern California) - Katja Filippova (EML Research) - Mark Dras (Macquarie University)
ORGANIZERS AND CONTACT: - James Clarke (University of Illinois at Urbana-Champaign) - Sebastian Riedel (University of Tokyo)
Email: ilpnlp2009 at gmail.com Website: http://www-tsujii.is.s.u-tokyo.ac.jp/ilpnlp/