Conceptual Language Models for Context-Aware Text Retrieval

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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 languageUndefined
Title of host publicationProceedings of the 13th Text REtrieval Conference Proceedings (TREC)
EditorsE.M Voorhees, Lori P. Buckland
Place of PublicationGaithersburg, Maryland, USA
PublisherNational Institute of Standards and Technology
Pages99
Number of pages8
ISBN (Print)not assigned
Publication statusPublished - May 2005
EventThirteenth Text REtrieval Conference, TREC-13 2004 - Gaithersburg, United States
Duration: 16 Nov 200419 Nov 2004
Conference number: 13

Publication series

NameNIST Special Publications
PublisherNational Institute of Standards and Technology (NIST)
VolumeSP 500-261

Conference

ConferenceThirteenth Text REtrieval Conference, TREC-13 2004
Abbreviated titleTREC
CountryUnited States
CityGaithersburg
Period16/11/0419/11/04

Keywords

  • EWI-7326
  • IR-63532
  • METIS-225950
  • DB-XMLIR: XML INFORMATION RETRIEVAL

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