Abstract
While participating in the HARD track our first
question was, what an IR-application should look
like that takes into account preference meta-data
from the user, without the need of explicit (manual)
meta-data tagging of the collection. Especially,
we touch the question how contextual information
can be described in an abstract model
appropriate for the IR-task, which further allows
improving and fine-tuning of the context representations
by learning from the user. As a first result,
we roughly sketch a system architecture and context
representation based on statistical language
models that fits well to the task of the HARD
track. Furthermore, we discuss issues of ranking
and score normalizations on this background.
Original language | Undefined |
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Title of host publication | Proceedings of the 13th Text REtrieval Conference Proceedings (TREC) |
Editors | E.M Voorhees, Lori P. Buckland |
Place of Publication | Gaithersburg, Maryland, USA |
Publisher | National Institute of Standards and Technology |
Pages | 99 |
Number of pages | 8 |
ISBN (Print) | not assigned |
Publication status | Published - May 2005 |
Event | Thirteenth Text REtrieval Conference, TREC-13 2004 - Gaithersburg, United States Duration: 16 Nov 2004 → 19 Nov 2004 Conference number: 13 |
Publication series
Name | NIST Special Publications |
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Publisher | National Institute of Standards and Technology (NIST) |
Volume | SP 500-261 |
Conference
Conference | Thirteenth Text REtrieval Conference, TREC-13 2004 |
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Abbreviated title | TREC |
Country/Territory | United States |
City | Gaithersburg |
Period | 16/11/04 → 19/11/04 |
Keywords
- EWI-7326
- IR-63532
- METIS-225950
- DB-XMLIR: XML INFORMATION RETRIEVAL