Hybrid models for future event prediction

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

Authors

We present a hybrid method to turn off-the-shelf information retrieval (IR) systems into future event predictors. Given a query, a time series model is trained on the publication dates of the retrieved documents to capture trends and periodicity of the associated events. The periodicity of historic data is used to estimate a probabilistic model to predict future bursts. Finally, a hybrid model is obtained by intertwining the probabilistic and the time-series model. Our empirical results on the New York Times corpus show that autocorrelation functions of time-series suffice to classify queries accurately and that our hybrid models lead to more accurate future event predictions than baseline competitors.
OriginalspracheEnglisch
TitelProceedings of the 20th ACM international conference on Information and knowledge management
HerausgeberBettina Berendt, Arjen de Vries, Wenfei Fan, Craig Macdonald, Iadh Ounis, Ian Ruthven
Anzahl der Seiten4
ErscheinungsortNew York
VerlagAssociation for Computing Machinery, Inc
Erscheinungsdatum2011
Seiten1981-1984
ISBN (elektronisch)978-145030717-8
DOIs
PublikationsstatusErschienen - 2011
Extern publiziertJa
Veranstaltung20th ACM Conference on Information and Knowledge Management - CIKM '11 - Glasgow, Großbritannien / Vereinigtes Königreich
Dauer: 24.10.201128.10.2011
http://www.cikm2011.org/

DOI

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