Term relevance weights in on-line information retrieval

Considerable evidence exists to show that the use of term relevance weights is beneficial in interactive information retrieval. Various term weighting systems are reviewed. An experiment is then described in which information retrieval users are asked to rank query terms in decreasing order of presumed importance prior to actual search and retrieval. The experimental design is examined, and various relevance ranking systems are evaluated, including fully automatic systems based on inverse document frequency parameters, human rankings performed by the user population, and combinations of the two.     

Automatic indexing using term discrimination and term precision measurements

A variety of abstract automatic indexing models have been developed in recent times in an effort to produce indexing methods that are both effective and usable in practice. Among these are the term discrimination model and the term precision system. These two indexing systems are briefly described and experimental evidence is cited showing that a combination of both theories produces better retrieval performance than either one alone. Appropriate conclusions are reached concerning viable automatic indexing procedures usable in practice.     

A comparison of two methods for boolean query relevancy feedback†

The relevance feedback process uses information derived from an initially retrieved set of documents to improve subsequent search formulations and retrieval output. In a Boolean query environment this implies that new query terms must be identified and Boolean operators must be chosen automatically to connect the various query terms. In this study two recently proposed automatic methods for relevance feedback of Boolean queries are evaluated and conclusions are drawn concerning the use of effective feedback methods in a Boolean query environment.     

Automatic query formulations in information retrieval.

Modern information retrieval systems are designed to supply relevant information in response to requests received from the user population. In most retrieval environments the search requests consist of keywords, or index terms, interrelated by appropriate Boolean operators. Since it is difficult for untrained users to generate effective Boolean search requests, trained search intermediaries are normally used to translate original statements of user need into useful Boolean search formulations. Methods are introduced in this study which reduce the role of the search intermediaries by making it possible to generate Boolean search formulations completely automatically from natural language statements provided by the system patrons. Frequency considerations are used automatically to generate appropriate term combinations as well as Boolean connectives relating the terms. Methods are covered to produce automatic query formulations both in a standard Boolean logic system, as well as in an extended Boolean system in which the strict interpretation of the connectives is relaxed. Experimental results are supplied to evaluate the effectiveness of the automatic query formulation process, and methods are described for applying the automatic query formulation process in practice.     

Automatic term class construction using relevance—A summary of work in automatic pseudoclassification

Term classifications and thesauri can be used for many purposes in automatic information retrieval. Normally a thesaurus is generated manually by subject experts: alternatively, the associations between the terms can be obtained automatically by using the occurrence characteristics of the terms across the documents of a collection. A third possibility consists in taking into account user relevance assessments of certain documents with respect to certain queries in order to build term classes designed to retrieve the relevant documents and simultaneously to reject the nonrelevant documents. This last strategy, known as pseudoclassification, produces a user-dependent term classification.A number of pseudoclassification studies are summarized in the present report, and conclusions are reached concerning the effectiveness and feasibility of constructing term classifications based on human relevance assessments.