Search (2 results, page 1 of 1)

0.010626211 = product of:
  0.026565526 = sum of:
    0.009978054 = product of:
      0.04989027 = sum of:
        0.04989027 = weight(_text_:problem in 3469) [ClassicSimilarity], result of:
          0.04989027 = score(doc=3469,freq=2.0), product of:
            0.17731056 = queryWeight, product of:
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.04177434 = queryNorm
            0.28137225 = fieldWeight in 3469, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              4.244485 = idf(docFreq=1723, maxDocs=44218)
              0.046875 = fieldNorm(doc=3469)
      0.2 = coord(1/5)
    0.016587472 = weight(_text_:of in 3469) [ClassicSimilarity], result of:
      0.016587472 = score(doc=3469,freq=12.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.25392252 = fieldWeight in 3469, product of:
          3.4641016 = tf(freq=12.0), with freq of:
            12.0 = termFreq=12.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3469)
  0.4 = coord(2/5)

Abstract

Ranking information retrieval (IR) systems with respect to their effectiveness is a crucial operation during IR evaluation, as well as during data fusion. This article offers a novel method of approaching the system-ranking problem, based on the widely studied idea of polyrepresentation. The principle of polyrepresentation suggests that a single information need can be represented by many query articulations-what we call query aspects. By skimming the top k (where k is small) documents retrieved by a single system for multiple query aspects, we collect a set of documents that are likely to be relevant to a given test topic. Labeling these skimmed documents as putatively relevant lets us build pseudorelevance judgments without undue human intervention. We report experiments where using these pseudorelevance judgments delivers a rank ordering of IR systems that correlates highly with rankings based on human relevance judgments.

Source

Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1081-1091

0.004282867 = product of:
  0.021414334 = sum of:
    0.021414334 = weight(_text_:of in 3688) [ClassicSimilarity], result of:
      0.021414334 = score(doc=3688,freq=20.0), product of:
        0.06532493 = queryWeight, product of:
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.04177434 = queryNorm
        0.32781258 = fieldWeight in 3688, product of:
          4.472136 = tf(freq=20.0), with freq of:
            20.0 = termFreq=20.0
          1.5637573 = idf(docFreq=25162, maxDocs=44218)
          0.046875 = fieldNorm(doc=3688)
  0.2 = coord(1/5)

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