Learning to select a time-aware retrieval model
Nattiya Kanhabua, Klaus Berberich, Kjetil Nørvåg · 2012
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.