Search (2 results, page 1 of 1)

Did you mean:
lcsh's%3a%22Machine %2f readable bibliographic data%22 2
lcsh's%3a%22Machine %2f readbale bibliographic data%22 2
lcshs%3a%22Machine %2f readable bibliographic data%22 2
lcsh's%3a%22Machine %2f readabie bibliographic data%22 2
lcshs%3a%22Machine %2f readbale bibliographic data%22 2

Efron, M.; Winget, M.: Query polyrepresentation for ranking retrieval systems without relevance judgments (2010) 0.00
```
0.0029865343 = product of:
  0.014932672 = sum of:
    0.014932672 = product of:
      0.029865343 = sum of:
        0.029865343 = weight(_text_:data in 3469) [ClassicSimilarity], result of:
          0.029865343 = score(doc=3469,freq=2.0), product of:
            0.14247625 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.04505818 = queryNorm
            0.2096163 = fieldWeight in 3469, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046875 = fieldNorm(doc=3469)
      0.5 = coord(1/2)
  0.2 = coord(1/5)
```
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.
Efron, M.: Linear time series models for term weighting in information retrieval (2010) 0.00
```
0.0029865343 = product of:
  0.014932672 = sum of:
    0.014932672 = product of:
      0.029865343 = sum of:
        0.029865343 = weight(_text_:data in 3688) [ClassicSimilarity], result of:
          0.029865343 = score(doc=3688,freq=2.0), product of:
            0.14247625 = queryWeight, product of:
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.04505818 = queryNorm
            0.2096163 = fieldWeight in 3688, product of:
              1.4142135 = tf(freq=2.0), with freq of:
                2.0 = termFreq=2.0
              3.1620505 = idf(docFreq=5088, maxDocs=44218)
              0.046875 = fieldNorm(doc=3688)
      0.5 = coord(1/2)
  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.