Search (2 results, page 1 of 1)
-
Efron, M.; Winget, M.: Query polyrepresentation for ranking retrieval systems without relevance judgments (2010)
0.02
0.02140484 = product of: 0.12842904 = sum of: 0.12842904 = weight(_text_:ranking in 3469) [ClassicSimilarity], result of: 0.12842904 = score(doc=3469,freq=6.0), product of: 0.20678882 = queryWeight, product of: 5.4090285 = idf(docFreq=537, maxDocs=44218) 0.038230307 = queryNorm 0.62106377 = fieldWeight in 3469, product of: 2.4494898 = tf(freq=6.0), with freq of: 6.0 = termFreq=6.0 5.4090285 = idf(docFreq=537, maxDocs=44218) 0.046875 = fieldNorm(doc=3469) 0.16666667 = coord(1/6)
- 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)
0.01
0.012358091 = product of: 0.07414854 = sum of: 0.07414854 = weight(_text_:ranking in 3688) [ClassicSimilarity], result of: 0.07414854 = score(doc=3688,freq=2.0), product of: 0.20678882 = queryWeight, product of: 5.4090285 = idf(docFreq=537, maxDocs=44218) 0.038230307 = queryNorm 0.35857132 = fieldWeight in 3688, product of: 1.4142135 = tf(freq=2.0), with freq of: 2.0 = termFreq=2.0 5.4090285 = idf(docFreq=537, maxDocs=44218) 0.046875 = fieldNorm(doc=3688) 0.16666667 = coord(1/6)
- 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.