Lee, C.; Lee, G.G.: Probabilistic information retrieval model for a dependence structured indexing system (2005)
0.01
0.013270989 = product of:
0.039812967 = sum of:
0.039812967 = product of:
0.079625934 = sum of:
0.079625934 = weight(_text_:indexing in 1004) [ClassicSimilarity], result of:
0.079625934 = score(doc=1004,freq=4.0), product of:
0.19018644 = queryWeight, product of:
3.8278677 = idf(docFreq=2614, maxDocs=44218)
0.049684696 = queryNorm
0.41867304 = fieldWeight in 1004, product of:
2.0 = tf(freq=4.0), with freq of:
4.0 = termFreq=4.0
3.8278677 = idf(docFreq=2614, maxDocs=44218)
0.0546875 = fieldNorm(doc=1004)
0.5 = coord(1/2)
0.33333334 = coord(1/3)
- Abstract
- Most previous information retrieval (IR) models assume that terms of queries and documents are statistically independent from each other. However, conditional independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence into a probabilistic retrieval model by adapting a dependency structured indexing system using a dependency parse tree and Chow Expansion to compensate the weakness of the assumption. In this paper, we describe a theoretic process to apply the Chow Expansion to the general probabilistic models and the state-of-the-art 2-Poisson model. Through experiments on document collections in English and Korean, we demonstrate that the incorporation of term dependences using Chow Expansion contributes to the improvement of performance in probabilistic IR systems.
