Information Access

Access to information is now recognised as a critical problem for business, media and the general public. Search is the best known application here, but cross-language access, question answering, peer to peer support, semantic annotation, information extraction, community and reputation analysis, and support tools for machine translation are other examples. Statistical methods, probabilistic and information theoretic models in information access have started to gain attention in the same way that statistical methods for natural language processing have become an important complement to the traditional linguistic approaches. These applications provide a key challenge to the general machine learning community both because of the depth of application experience required, and to integrate properly with successful traditional approaches. Significant challenges are provided by constantly evolving hardware environments and novel user scenarios related to issues like the rise of mobile devices as an information access platform, and the increasing need for cross-media and cross-language services.

Workshop Aims

The aim of this workshop is to bring together researchers in this emerging community in an informal setting to promote discussion and the cross fertilisation of ideas and methods. Position statements and reviews of the state of the art are encouraged, as well as research work.