Learning to select a time-aware retrieval model

Authors:
Nattiya Kanhabua;Klaus Berberich;Kjetil Nørvåg
Affiliations:
Leibniz Universität Hannover, Hannover, Germany;Max-Planck Institute for Informatics, Saarbrücken, Germany;Norwegian University of Science and Technology, Trondheim, Norway
Venue:
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Year:
2012

Citing 5
Cited 0

Predicting query performance

SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Temporal profiles of queries

ACM Transactions on Information Systems (TOIS)
Determining time of queries for re-ranking search results

ECDL'10 Proceedings of the 14th European conference on Research and advanced technology for digital libraries
A language modeling approach for temporal information needs

ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval
Learning to select a ranking function

ECIR'2010 Proceedings of the 32nd European conference on Advances in Information Retrieval

Quantified Score

Hi-index	0.00

Visualization

Abstract

Time-aware retrieval models exploit one of two time dimensions, namely, (a) publication time or (b) content time (temporal expressions mentioned in documents). We show that the effectiveness for a temporal query (e.g., illinois earthquake 1968) depends significantly on which time dimension is factored into ranking results. Motivated by this, we propose a machine learning approach to select the most suitable time-aware retrieval model for a given temporal query. Our method uses three classes of features obtained from analyzing distributions over two time dimensions, a distribution over terms, and retrieval scores within top-k result documents. Experiments on real-world data with crowdsourced relevance assessments show the potential of our approach.