SODA

Acquiring (and Using) Linguistic (and World) Knowledge for Information Access

Karlgren, Jussi and Gambäck, Björn and Kanerva, Pentti (2002) Acquiring (and Using) Linguistic (and World) Knowledge for Information Access. Tech Report (SS-02-09). AAAI Press, Menlo Park. ISBN 1-57735-186-X

Full text not available from this repository.

Abstract

Information access tasks need flexible text understanding. While full text understanding remains a distant and possibly unattainable goal, to deliver better information access performance we must advance content analysis beyond the simple algorithms used today--and 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 defining an inspectable and transparent model and developing an understanding of knowledge-intensive interaction. Text understanding needs a theory. Knowledge modeling, semantics, or ontology construction are areas marked by the absence of significant consensus either in points of theory or scope of application. Even the terminology and success criteria of the somewhat overlapping fields are fragmented. Some approaches to content modeling lay claim to psychological realism, others to inspectability; some are portable, others transparent; some are robust, others logically sound; some efficient, others scalable. Information access tasks give focus to modeling. It is too much to hope for a set of standards to emerge from the intellectually fairly volatile and fragmented area of semantics or cognitive modeling. But in our application areas -- namely, those in the general field of information access - external success criteria are better established. Compromise from theoretical underpinnings in the name of performance. Information access tasks need flexible text understanding. While full text understanding remains a distant and possibly unattainable goal, to deliver better information access performance we must advance content analysis beyond the simple algorithms used today--and the dynamic nature of both information needs and information sources will make a flexible model or set of models a necessity.

Item Type:Book
Subjects:I. Computing Methodologies > I.2 ARTIFICIAL INTELLIGENCE
ID Code:47
Deposited By:Userware Researcher
Deposited On:07 Sep 2009
Last Modified:18 Nov 2009 15:51

Repository Staff Only: item control page