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The performance of probabilistic information retrieval systems is studied where differing statistical dependence assumptions are used when estimating the probabilities inherent in the retrieval model. Experimental results using the Bahadur Lazarsfeld expansion suggest that the greatest degree of performance increase is achieved by incorporating term dependence information in estimating . It is suggested that incorporating dependence in to degree 3 be used; incorporating more dependence information results in relatively little increase in performance. Experiments examine the span of dependence in natural language text, the window of terms in which dependencies are computed and their effect on information retrieval performance. Results provide additional support for the notion of a window of to terms in width; terms in this window may be most useful when computing dependence. 2 1 Introduction Those who study information retrieval often assume that the features or terms use...

The Principle of Maximum Entropy is discussed and two classic probabilistic models of information retrieval, the Binary Independence Model of Robertson and Sparck Jones and the Combination Match Model of Croft and Harper are derived using the maximum entropy approach. The assumptions on which the classical models are based are not made. In their place, the probability distribution of maximum entropy consistent with a set of constraints is determined. It is argued that this subjectivist approach is more philosophically coherent than the frequentist conceptualization of probability that is often assumed as the basis of probabilistic modeling and that this philosophical stance has important practical consequences with respect to the realization of information retrieval research.

Monitoring user-system performance in interactive search is a challenging task. Traditional measures of retrieval evaluation, based on recall and precision, are not of any use in real time, for they require a priori knowledge of relevant documents. This paper shows how a Shannon entropy-based measure of user-system performance naturally falls in the framework of (interactive) probabilistic information retrieval. The value of entropy of the distribution of probability of relevance associated with the documents in the collection can be used to monitor search progress in live testing, to allow for example the system to select an optimal combination of search strategies. User profiling and tuning parameters of retrieval systems are other important applications.

statistically independent from each another. However, independence assumption is obviously and openly understood to be wrong, so we present a new method of incorporating term dependence in probabilistic retrieval model by adapting a structural index system using dependency parse tree and the 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, and we re-examine the weight of phrase terms. Through the experiments on document collections, ETRI-KEMONG in Korean, we demonstrate that the incorporation of term dependences using the Chow Expansion contribute to the improvement of performance in Probabilistic IR systems. Keywords term dependence, phrasal indexing, Chow Expansion, probabilistic model, 2-Poisson model 1.

The maximum entropy principle may be applied to the design of probabilistic retrieval systems. When there are inconsistent expert judgments, the resulting optimization problem cannot be solved. The inconsistency of the expert judgments can be revealed by solving a linear programming formulation. In the case of inconsistent judgment, four plausible schemes are proposed in order to find revised judgments which are consistent with the true data structure but still reflect the original expert judgment. These schemes are the Interactive, Minimum