London, 18 October 2008

We aim to bring together people from different research communities interested in exploring how corpus characteristics affect the behaviour of techniques in information retrieval and natural language processing, and to set out a roadmap for a shared research agenda.

It is well known in NLP and IR that the effectiveness of a technique depends on both the data on which it is deployed and its match with the task at hand. In 1973, Spärck-Jones attributed differing degrees of success at automatic classification to differences in dataset characteristics. Since Croft and Harper (1979), IR performance has repeatedly been related to collection size and other features, though no upper bound has been found.

The importance of data and task dependencies has been highlighted in IR, anaphora resolution, automatic summarization and recently, in word sense disambiguation. Many web/enterprise web retrieval systems rely on URL properties, link graph properties, click streams, and so on, with performance dependent on the degree to which this evidence is present and meaningful in a particular corpus.

Full synopsis - Editors - Papers - Invited Talks

This conference was sponsored by

BCS IRSG