Efron, M.: Linear time series models for term weighting in information retrieval (2010)
0.01
0.005361108 = product of:
0.037527755 = sum of:
0.012107591 = weight(_text_:information in 3688) [ClassicSimilarity], result of:
0.012107591 = score(doc=3688,freq=8.0), product of:
0.052020688 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.029633347 = queryNorm
0.23274569 = fieldWeight in 3688, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=3688)
0.025420163 = weight(_text_:retrieval in 3688) [ClassicSimilarity], result of:
0.025420163 = score(doc=3688,freq=4.0), product of:
0.08963835 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.029633347 = queryNorm
0.2835858 = fieldWeight in 3688, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=3688)
0.14285715 = coord(2/14)
- 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
Efron, M.; Winget, M.: Query polyrepresentation for ranking retrieval systems without relevance judgments (2010)
0.01
0.005129378 = product of:
0.035905644 = sum of:
0.0104854815 = weight(_text_:information in 3469) [ClassicSimilarity], result of:
0.0104854815 = score(doc=3469,freq=6.0), product of:
0.052020688 = queryWeight, product of:
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.029633347 = queryNorm
0.20156369 = fieldWeight in 3469, product of:
2.4494898 = tf(freq=6.0), with freq of:
6.0 = termFreq=6.0
1.7554779 = idf(docFreq=20772, maxDocs=44218)
0.046875 = fieldNorm(doc=3469)
0.025420163 = weight(_text_:retrieval in 3469) [ClassicSimilarity], result of:
0.025420163 = score(doc=3469,freq=4.0), product of:
0.08963835 = queryWeight, product of:
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.029633347 = queryNorm
0.2835858 = fieldWeight in 3469, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.024915 = idf(docFreq=5836, maxDocs=44218)
0.046875 = fieldNorm(doc=3469)
0.14285715 = coord(2/14)
- 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
