Linear time series models for term weighting in information retrieval

This paper proposes capturing each term’s collection frequency at discrete time intervals over the lifespan of a corpus and analyzing the resulting time series, and induces three time-based measures of term importance and test these against state-of-the-art term weighting models.

Abstract

Abstract Common measures of term importance in information retrieval (IR) rely on counts of term frequency; rare terms receive higher weight in document ranking than common terms receive. However, realistic scenarios yield additional information about terms in a collection. Of interest in this article is the temporal behavior of terms as a collection changes over time. We propose capturing each term's collection frequency at discrete time intervals over the lifespan of a corpus and analyzing the resulting time series. We hypothesize the collection frequency of a weakly discriminative term x at time t is predictable by a linear model of the term's prior observations. On the other hand, a linear time series model for a strong discriminators' collection frequency will yield a poor fit to the data. Operationalizing this hypothesis, we induce three time‐based measures of term importance and test these against state‐of‐the‐art term weighting models.