Treffer: Automatic routing and retrieval using smart: TREC-2

Title:
Automatic routing and retrieval using smart: TREC-2
Source:
TREC-2 : text retrieval conferenceInformation processing & management. 31(3):315-326
Publisher Information:
Oxford: Elsevier Science, 1995.
Publication Year:
1995
Physical Description:
print, 15 ref
Original Material:
INIST-CNRS
Subject Terms:
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Cornell univ., dep. computer sci., Ithaca NY 14853, United States
ISSN:
0306-4573
Rights:
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.3601531
Database:
PASCAL Archive

Weitere Informationen

The SMART information retrieval project emphasizes completely automatic approaches to the understanding and retrieval of large quantities of text. We continue our work in the TREC 2 environment, performing both routing and ad-hoc experiments. The ad-hoc work extends our investigations into combining global similarities, giving an overall indication of how a document matches a query, with local similarities identifying a smaller part of the document that matches the query. The performance of the ad-hoc runs is good, but it is clear we are not yet taking full advantage of the available local information. Our routing experiments use conventional relevance feedback approaches to routing, but with a much greater degree of query expansion than was previously done. The length of a query vector is increased by a factor of 5 to 10 by adding terms found in previously seen relevant documents. This approach improves effectiveness by 30-40% over the original query.