de.mpg.escidoc.pubman.appbase.FacesBean
Deutsch
 
Hilfe Wegweiser Impressum Kontakt Einloggen
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Bericht

A Language Modeling Approach for Temporal Information Needs

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons44119

Berberich,  Klaus
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44104

Bedathur,  Srikanta
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44000

Alonso,  Omar
Databases and Information Systems, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45720

Weikum,  Gerhard
Databases and Information Systems, MPI for Informatics, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

mpi-i-2010-5-001.pdf
(beliebiger Volltext), 2MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Berberich, K., Bedathur, S., Alonso, O., & Weikum, G.(2010). A Language Modeling Approach for Temporal Information Needs (MPI-I-2010-5-001). Saarbrücken: Max-Planck-Institut für Informatik. Retrieved from http://domino.mpi-inf.mpg.de/internet/reports.nsf/NumberView/2010-5-001.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0014-65AB-C
Zusammenfassung
This work addresses information needs that have a temporal dimension conveyed by a temporal expression in the user's query. Temporal expressions such as \textsf{``in the 1990s''} are frequent, easily extractable, but not leveraged by existing retrieval models. One challenge when dealing with them is their inherent uncertainty. It is often unclear which exact time interval a temporal expression refers to. We integrate temporal expressions into a language modeling approach, thus making them first-class citizens of the retrieval model and considering their inherent uncertainty. Experiments on the New York Times Annotated Corpus using Amazon Mechanical Turk to collect queries and obtain relevance assessments demonstrate that our approach yields substantial improvements in retrieval effectiveness.