Treffer: TREC-3 Ad-Hoc, routing retrieval and thresholding experiments using PIRCS

Title:
TREC-3 Ad-Hoc, routing retrieval and thresholding experiments using PIRCS
Source:
TREC-3: text retrieval conferenceNIST special publication. (500225):247-255
Publisher Information:
Gaithersburg, MD: National Institute of Standards and Technology, 1995.
Publication Year:
1995
Physical Description:
print, 16 ref
Original Material:
INIST-CNRS
Document Type:
Konferenz Conference Paper
File Description:
text
Language:
English
Author Affiliations:
CUNY, Queens coll., computer sci. dep., Flushing NY 11367, 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.2484566
Database:
PASCAL Archive

Weitere Informationen

The PIRCS retrieval system has been upgrated in TREC-3 to handle the full English collections of 2 GB in an efficient manner. For ad-hoc retrieval, we use recurrent spreading of activation in our network to implement query learning and expansion based on the best-ranked subdocuments of an initial retrieval. We also augment our standard retrieval algorithm with a soft-Boolean component. For routing, we use learning from signal-rich short documents or subdocument segments. For the optional thresholding experiment, we tried two approches to transforming retrieval status values (RSV's) so that they could be used to partition documents into retrieved and nonretrieved sets. The first method normalzies RSV's using a query self-retrieval score. The second, which requires training data, uses logistic regression to convert RSV's into estimates of probability of relevance. Overall, our results are highly competitive with those of other participants.