Treffer: Automatic Query expansion using SMART : TREC 3

Title:
Automatic Query expansion using SMART : TREC 3
Source:
TREC-3: text retrieval conferenceNIST special publication. (500225):69-80
Publisher Information:
Gaithersburg, MD: National Institute of Standards and Technology, 1995.
Publication Year:
1995
Physical Description:
print, 17 ref
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Cornell univ., dep. computer sci., Ithaca NY 14853-7501, United States
ISSN:
1048-776X
Rights:
Copyright 1997 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.2485150
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 TREC 3, performing runs in the routing, ad-hoc, and foreign language environments. Our major focus is massive query expansion : adding from 300 to 530 terms to each query. These terms come from known relevant documents in the case of routing, and from just the top retrieved documents in the case of ad-hoc and Spanish. This approach improves effectiveness from 7% to 25% in the various experiments. Other 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 which matches the query. Using an overlapping text window definition of local, we achieve a 16% improvement.