Search (3 results, page 1 of 1)

  • × author_ss:"Efron, M."
  • × theme_ss:"Retrievalalgorithmen"
  1. Efron, M.: Linear time series models for term weighting in information retrieval (2010) 0.00 
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    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.
    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.7, S.1299-1312
  2. Efron, M.: Query expansion and dimensionality reduction : Notions of optimality in Rocchio relevance feedback and latent semantic indexing (2008) 0.00 
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    Abstract
    Rocchio relevance feedback and latent semantic indexing (LSI) are well-known extensions of the vector space model for information retrieval (IR). This paper analyzes the statistical relationship between these extensions. The analysis focuses on each method's basis in least-squares optimization. Noting that LSI and Rocchio relevance feedback both alter the vector space model in a way that is in some sense least-squares optimal, we ask: what is the relationship between LSI's and Rocchio's notions of optimality? What does this relationship imply for IR? Using an analytical approach, we argue that Rocchio relevance feedback is optimal if we understand retrieval as a simplified classification problem. On the other hand, LSI's motivation comes to the fore if we understand it as a biased regression technique, where projection onto a low-dimensional orthogonal subspace of the documents reduces model variance.
    Source
    Information processing and management. 44(2008) no.1, S.163-180
  3. Efron, M.; Winget, M.: Query polyrepresentation for ranking retrieval systems without relevance judgments (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1081-1091

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