Paper Info
Title
Probabilistic information retrieval model for a dependency structured indexing system
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.
Year
DOI
Venue
2005
10.1016/j.ipm.2003.11.001
Inf. Process. Manage.
Keywords
Field
DocType
probabilistic retrieval model,information retrieval,indexing system,key words,chow expansion,previous information retrieval,term dependence,general probabilistic model,dependency parse tree,probabilistic information retrieval model,2-poisson model,probabilistic model,document collection,conditional independence assumption,probabilistic ir system,indexation,dependency parsing,poisson model,statistical independence
Data mining,Divergence-from-randomness model,Parse tree,Computer science,Conditional independence,Search engine indexing,Theoretical computer science,Statistical model,Artificial intelligence,Probabilistic logic,Probabilistic relevance model,Automatic indexing
Journal
Volume
Issue
ISSN
41
2
Information Processing and Management
Citations 
PageRank 
References 
13
0.68
16
Authors
2
Name
Order
Citations
PageRank
Changki Lee127926.18
Gary Geunbae Lee293293.23