%0 Conference Paper %1 Kanhabua:2012:LST:2348283.2348488 %A Kanhabua, Nattiya %A Berberich, Klaus %A Nørv\aag, Kjetil %B Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval %C New York, NY, USA %D 2012 %I ACM %K myown %P 1099--1100 %R 10.1145/2348283.2348488 %T Learning to select a time-aware retrieval model %U http://doi.acm.org/10.1145/2348283.2348488 %X Time-aware retrieval models exploit one of two time dimensions, namely, (a) <i>publication time</i> or (b) <i>content time</i> (temporal expressions mentioned in documents). We show that the effectiveness for a <i>temporal query</i> (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-<i>k</i> result documents. Experiments on real-world data with crowdsourced relevance assessments show the potential of our approach. %@ 978-1-4503-1472-5

@inproceedings{Kanhabua:2012:LST:2348283.2348488, abstract = {Time-aware retrieval models exploit one of two time dimensions, namely, (a) <i>publication time</i> or (b) <i>content time</i> (temporal expressions mentioned in documents). We show that the effectiveness for a <i>temporal query</i> (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-<i>k</i> result documents. Experiments on real-world data with crowdsourced relevance assessments show the potential of our approach.}, acmid = {2348488}, added-at = {2012-12-13T13:02:33.000+0100}, address = {New York, NY, USA}, author = {Kanhabua, Nattiya and Berberich, Klaus and N{\o}rv{\aa}g, Kjetil}, biburl = {https://www.bibsonomy.org/bibtex/2170c9666d30ff36b8db84545001f9b8c/nattiya}, booktitle = {Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval}, description = {Learning to select a time-aware retrieval model}, doi = {10.1145/2348283.2348488}, interhash = {e9a1e82fac7044eec82e1708c47f572d}, intrahash = {170c9666d30ff36b8db84545001f9b8c}, isbn = {978-1-4503-1472-5}, keywords = {myown}, location = {Portland, Oregon, USA}, numpages = {2}, pages = {1099--1100}, publisher = {ACM}, series = {SIGIR '12}, timestamp = {2012-12-13T13:09:17.000+0100}, title = {Learning to select a time-aware retrieval model}, url = {http://doi.acm.org/10.1145/2348283.2348488}, year = 2012 }