Document (#35689)

Author

Efron, M.

Title

Linear time series models for term weighting in information retrieval

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.7, S.1299-1312

Year

2010

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.

Theme

Retrievalalgorithmen

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