Copyright 1995 INIST-CNRS CC BY 4.0 Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Sciences of information and communication. Documentation
FRANCIS
Accession Number:
edscal.3459612
Database:
PASCAL Archive
Weitere Informationen
The ARPA TIPSTER project which is the source of the data and funding for TREC, has involved four sites in the area of text retrieval and routing. The TIPSTER project in the information Retrieval of the Computer Science Department, University of Massachussetts, Amherst (which includes MCC as a subcontractor), has focused on the following goals: improving the effectiveness of information retrieval techniques for large, full-text databases; 2: improving the effectiveness of routing techiques appropriate for long-term information needs; 3- Demonstrating the effectiveness of these retrieval and routinq techniques for Japanese full text database. Our general approach to achieve these goals has been to use improved representations of text and information needs in the framework of a new model of retrieval. This model uses Bayesian netwoks to describe how text and queries should be uses to identify relevant document. Retrieval (and routing) is viewed as a probabilistic inference process whic compares text representations based on different forms of linguistic and statistical evidence to representations of information needs. Learnin techniques are uses to modify the initial query both for short-terme and long-term information needs (relevance feedback and routing, respectively).
