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.
This conference was sponsored by
BCS IRSG
The Workshop on Corpus Profiling for Information Retrieval and Natural Language Processing took place in London, in October 2008, in conjunction with IIiX2008. Our aim was to bring together people from different research communities interested in exploring how specific properties of a corpus or collection affect the behaviour of techniques in Information Retrieval (IR) and Natural Language Processing (NLP), and to start mapping out a shared research agenda. These eWiCs Proceedings capture the final versions of papers presented at the workshop.
