Relevance feedback is an automatic process, introduced over 20 years ago, designed to produce improved query formulations following an initial retrieval operation. The principal relevance feedback methods described over the years are examined briefly, and evaluation data are included to demonstrate the effectiveness of the various methods. Prescriptions are given for conducting text re-trieval operations iteratively using relevance feedback. Introduction to Relevance Feedback It is well known that the original query formulation process is not transparent to most information system users. In particular, without detailed knowledge of the collection make-up, and of the retrieval environment, most users find

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The 2–Poisson model for term frequencies is used to suggest ways of incorporating certain variables in probabilistic models for information retrieval. The variables concerned are within-document term frequency, document length, and within-query term frequency. Simple weighting functions are developed, and tested on the TREC test collection. Considerable performance improvements (over simple inverse collection frequency weighting) are demonstrated. 1

The naive Bayes classifier, currently experiencing a renaissance in machine learning, has long been a core technique in information retrieval. We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assump- tions made about word occurrences in documents.

The paper combines a comprehensive account of the 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.

The effects of adding information from relevant documents are examined in the TREC routing environment. A modified Rocchio relevance feedback approach is used, with a varying number of relevant documents retrieved by an initial SMART search, and a varying number of terms from those relevant documents used to expand the initial query. Recall-precision evaluation reveals that as the amount of expansion of the query due to adding terms from relevant documents increases, so does the effectiveness. It is observed for this particular experiment that there seems to be a linear relationship between the log of the number of terms added and the recall-precision effectiveness. There also seems to be a linear relationship between the log of the number of known relevant documents and the recall-precision effectiveness. 1 Introduction Relevance feedback is a commonly accepted method of improving interactive retrieval effectiveness.[1, 2] An initial search is made by the system with a user-supplied ...

We introduce and create a framework for deriving probabilistic models of Information Retrieval. The models are nonparametric models of IR obtained in the language model approach. We derive term-weighting models by measuring the divergence of the actual term distribution from that obtained under a random process. Among the random processes we study the binomial distribution and Bose–Einstein statistics. We define two types of term frequency normalization for tuning term weights in the document–query matching process. The first normalization assumes that documents have the same length and measures the information gain with the observed term once it has been accepted as a good descriptor of the observed document. The second normalization is related to the document length and to other statistics. These two normalization methods are applied to the basic models in succession to obtain weighting formulae. Results show that our framework produces different nonparametric models forming baseline alternatives to the standard tf-idf model.

We describe a method for probabilistic document indexing using relevance feedback data that has been collected from a set of queries. Our approach is based on three new concepts: (1) Abstraction from specific terms and documents, which overcomes the restriction of limited relevance information for parameter estimation. (2) Flexibility of the representation, which allows the integration of new text analysis and knowledge-based methods in our approach as well as the consideration of document structures or different types of terms. (3) Probabilistic learning or classification methods for the estimation of the indexing weights making better use of the available relevance information. Our approach can be applied under restrictions that hold for real applications. We give experimental results for five test collections which show improvements over other indexing methods.

Abstract- in this article three retrieval models for probabilistic indexing are described along with evaluation results for each. First is the binary independence indexing @II) model, which is a generalized version of the Maron and Kuhns indexing model. In this model, the indexing weight of a descriptor in a document is an estimate of the proba-bility of relevance of this document with respect to queries using this descriptor. Sec-ond is the retrieval-with-probabilistic-indexing (RPI) model, which is suited to different kinds of probabilistic indexing. For that we assume that each indexing scheme has its own concept of “correctness ” to which the probabilities relate. In addition to the prob-abilistic indexing weights, the RPI model provides the possibility of reIevance weight-ing of search terms. A third mode1 that is similar was proposed by Croft some years ago as an extension of the binary independence retrieval model but it can be shown that this model is not based on the probabilistic ranking principle. The probabilistic indexing weights required for any of these models can be provided by an application of the Darm-stadt indexing approach (DIA) for indexing with descriptors from a controlled vocabu-Iary. The experimental results show signi~cant improvements over retrieval with binary indexing. Finally, suggestions are made regarding how the DIA can be applied to prob-abilistic indexing with free text terms. 1.

Discriminative models have been preferred over generative models in many machine learning problems in the recent past owing to some of their attractive theoretical properties. In this paper, we explore the applicability of discriminative classifiers for IR. We have compared the performance of two popular discriminative models, namely the maximum entropy model and support vector machines with that of language modeling, the state-of-the-art generative model for IR. Our experiments on ad-hoc retrieval indicate that although maximum entropy is significantly worse than language models, support vector machines are on par with language models. We argue that the main reason to prefer SVMs over language models is their ability to learn arbitrary features automatically as demonstrated by our experiments on the home-page finding task of TREC-10.