To appear at TAIA2013 (a SIGIR 2013 workshop).

Document filtering over time is widely applied in various tasks such as tracking topics in online news or social media. We consider it a classification task, where topics of interest correspond to classes, and the feature space consists of the words associated to each class. In “streaming” settings the set of words associated with a concept may change. In this paper we employ a multinomial Naive Bayes classifier and perform periodic feature selection to adapt to evolving topics. We propose two ways of employing Pearson’s χ² test for feature selection and demonstrate its benefit on the TREC KBA 2012 data set. By incorporating a time-dependent function in our equations for χ² we provide an elegant method for applying different weighting schemes. Experiments show improvements of our approach over a non-adaptive baseline.