A probabilistic model of information retrieval:development and comparative experiments
ABSTRACT
The paper combines a comprehensive account of a probabilistic model of retrieval with new systematic experiments on TREC Programme material. It presents the model from its foundations through its logical development to cover more aspects of retrieval data and a wider range of system functions. Each step in the argument is matched by comparative retrieval tests, to provide a single coherent account of a major line of research. The experiments demonstrate, for a large test collection, that the probabilistic model is effective and robust, and that it responds appropriately, with major improvements in performance, to key features of retrieval situations. Part 1 covers the foundations and the model development for document collection and relevance data, along with the test apparatus. Part 2 covers the further development and elaboration of the model, with extensive testing, and briefly considers other environment conditions and tasks, model training, concluding with comparisons with other approaches and an overall assessment.
INTRODUCTION
The probabilistic approach to retrieval was first presented in Maron and Kuhns (1960). Since then it has been elaborated in different ways, tested and applied, especially in work by Maron and Cooper, by van Rijsbergen and his associates, by Croft and Turtle, by Fuhr, and by Robertson and his colleagues at City University. As implemented in the City Okapi system it has been subjected to heavy testing in the very large evaluation programme represented by the (D)ARPA/NIST Text REtrieval Conferences (TRECs). The literature on the probabilistic approach, even just that due to the authors mentioned, is by now extensive and as it is often also densely technical, it is hard to see the wood for the trees. There is however, by now, a well-understood core theory and well-established practical experience in exploiting this theory. Thus the probabilistic model that has been developed and applied at City has a firm grounding and demonstrated utility. This paper is intended to give a unified and accessible account of this particular model. It will show how the model treats retrieval concepts and responds to retrieval situations, and how the formal analysis on which the claim for the value of this approach to retrieval is based is supported by empirical evidence from substantial performance tests. It should be noted that there are now several distinct versions of the probabilistic approach, in effect several different probabilistic models of information retrieval. This paper is primarily concerned with what we will for convenience label the City model, initially proposed in Robertson and Sparck Jones (1976), and subsequently developed to accommodate test findings and to meet an increasing range of retrieval circumstances and environments. Hereafter in this paper we will use “the probabilistic approach” to refer to the class of models and “the probabilistic model” to refer specifically to the City model. The presentation of the model has some historical reference, but we have organised the paper primarily to proceed logically from a simple starting point to a more complex reality, as follows. We begin in Section 2, Foundations, with the basic elements of the general probabilistic model, providing just enough apparatus to motivate its subsequent specific interpretation. The key notions here are probability of relevance of a document to a user need, and hence of ranking documents on this basis. In Section 3, Test collections and measures, we present the data and performance measures used for the experiments associated with the development of the model in subsequent sections. We begin this development in Section 4, Data, by considering the specific types of information that are available to interpret the very abstract model introduced in Section 2. These are, naturally, facts about the occurrences of retrieval entities of various kinds: terms, documents, etc.
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