Search (3 results, page 1 of 1)

  • × author_ss:"Efron, M."
  • × year_i:[2000 TO 2010}
  1. Efron, M.: Shannon meets Shortz : a probabilistic model of crossword puzzle difficulty (2008) 0.00 
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    Abstract
    This article is concerned with the difficulty of crossword puzzles. A model is proposed that quantifies the difficulty of a Puzzle P with respect to its clues. Given a clue-answer pair (c,a), we model the difficulty of guessing a based on c using the conditional probability P(a based on c); easier mappings should enjoy a higher conditional probability. The model is tested by two experiments, each of which involves estimating the difficulty of puzzles taken from The New York Times. Additionally, we discuss how the notion of information implicit in our model relates to more easily quantifiable types of information that figure into crossword puzzles.
    Type
    a
  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.
    Type
    a
  3. Efron, M.: Eigenvalue-based model selection during Latent Semantic Indexing (2005) 0.00 
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    Abstract
    In this study amended parallel analysis (APA), a novel method for model selection in unsupervised learning problems such as information retrieval (IR), is described. At issue is the selection of k, the number of dimensions retained under latent semantic indexing (LSI). Amended parallel analysis is an elaboration of Horn's parallel analysis, which advocates retaining eigenvalues larger than those that we would expect under term independence. Amended parallel analysis operates by deriving confidence intervals an these "null" eigenvalues. The technique amounts to a series of nonparametric hypothesis tests an the correlation matrix eigenvalues. In the study, APA is tested along with four established dimensionality estimators an six Standard IR test collections. These estimates are evaluated with regard to two IR performance metrics. Additionally, results from simulated data are reported. In both rounds of experimentation APA performs weIl, predicting the best values of k an 3 of 12 observations, with good predictions an several others, and never offering the worst estimate of optimal dimensionality.
    Type
    a