To move forward the research frontier in the general field of information access, one of the bottlenecks we need to address is understanding textual content somewhat better. While full text understanding remains a distant and possibly unattainable goal, advances in content analysis beyond the simple word-occurrence statistics or name-recognition algorithms used today would seem to be desirable. Information retrieval is a blunt information access task, and information-retrieval systems deliver useful results with a simple text and content model. Much better models are necessary for information access tasks that involve information refinement, meaning tasks that involve processing information in text--and some specific questions in information retrieval proper are fairly knowledge- intensive such as query expansion or questions related to multilinguality. In addition, the dynamic nature of both information needs and information sources will make a flexible model or set of models a necessity. Models must either be adaptive or easily adapted by some form of low-cost intervention; and they must support incremental knowledge build-up. The first requirement involves acquisition of information from unstructured data; the second involves finding an inspectable and transparent model and developing an understanding of knowledge-intensive interaction.