Search (13 results, page 1 of 1)

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Abstract

The performance of an information retrieval or text and media filtering system may be determined through analytic methods as well as by traditional simulation or experimental methods. These analytic methods can provide precise statements about expected performance. They can thus determine which of 2 similarly performing systems is superior. For both a single query terms and for a multiple query term retrieval model, a model for comparing the performance of different probabilistic retrieval methods is developed. This method may be used in computing the average search length for a query, given only knowledge of database parameter values. Describes predictive models for inverse document frequency, binary independence, and relevance feedback based retrieval and filtering. Simulation illustrate how the single term model performs and sample performance predictions are given for single term and multiple term problems

Date

22. 2.1996 13:14:10

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Abstract

A document classifier places documents together in a linear arrangement for browsing or high-speed access by human or computerised information retrieval systems. Requirements for document classification and browsing systems are developed from similarity measures, distance measures, and the notion of subject aboutness. A requirement that documents be arranged in decreasing order of similarity as the distance from a given document increases can often not be met. Based on these requirements, information-theoretic considerations, and the Gray code, a classification system is proposed that can classifiy documents without human intervention. A measure of classifier performance is developed, and used to evaluate experimental results comparing the distance between subject headings assigned to documents given classifications from the proposed system and the Library of Congress Classification (LCC) system

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Abstract

The best-case, random and worst-case document rankings and retrieval performance may be determined using a method discussed here. Knowledge of the best case performance allows users and system designers to determine how close to the optimum condition their search is and select queries and matching functions that will produce the best results. Suggests a method for deriving the optimal Boolean query for a given level of recall and a method for determining the quality of a Boolean query. Measures are proposed that modify conventional text retrieval measures such as precision, E, and average search length, so that the values for these measures are 1 when retrieval is optimal, 0 when retrieval is random, and -1 when worst-case. Tests using one of these measures show that many retrieval are optimal? Consequences for retrieval research are examined

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Abstract

The grammars of natural languages may be learned by using genetic algorithms that reproduce and mutate grammatical rules and parts of speech tags, improving the quality of later generations of grammatical components. Syntactic rules are randomly generated and then evolve; those rules resulting in improved parsing and occasionally improved filtering performance are allowed to further propagate. The LUST system learns the characteristics of the language or subkanguage used in document abstracts by learning from the document rankings obtained from the parsed abstracts. Unlike the application of traditional linguistic rules to retrieval and filtering applications, LUST develops grammatical structures and tags without the prior imposition of some common grammatical assumptions (e.g. part of speech assumptions), producing grammars that are empirically based and are optimized for this particular application

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Abstract

Studies the performance of probabilistic information retrieval systems where differing statistical dependence assumptions are used when estimating the probabilities inherent in the retrieval model. Uses the Bahadur Lazarsfeld expansion model

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Abstract

An analytic method of information retrieval and filtering evaluation can quantitatively predict the expected number of documents examined in retrieving a relevant document. It also allows researchers and practioners to qualitatively understand how varying different estimates of query parameter values affects retrieval performance. The incoorporation of relevance feedback to increase our knowledge about the parameters of relevant documents and the robustness of parameter estimates is modeled. Single term and two term independence models, as well as a complete term dependence model, are developed. An economic model of retrieval performance may be used to study the effects of database size and to provide analytic answers to questions comparing retrieval from small and large databases, as well as questions about the number of terms in a query. Results are presented as a performance surface, a three dimensional graph showing the effects of two independent variables on performance.

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Abstract

Patrons often browse through books organized by a library classification system, looking for books to use and possibly circulate. This research is an examination of the clustering of similar books provided by a classification system and ways in which the books that patrons circulate are clustered. Measures of classification system performance are suggested and used to evaluate two test collections. Regression formulas are derived describing the relationships among the number of areas in which books were found (the number of stops a patron makes when browsing), the distances across a cluster, and the average number of books a patron circulates. Patrons were found usually to make more stops than there were books found at their average stop. Consequences for full-text document systems and online catalogs are suggested

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Abstract

Suggests a method for comparison of the use of Boolean queries and ranking documents using document and term weights, and examines their relative merits. The performance of information retrieval may be determined either by using experimental simulation, or through the application of analytic techniques that estimate the retrieval performance, given values for query and database characteristics. Using these performance predicting techniques, sample performance figures are provided for queries using the Boolean operators and, and or, as well as for probabilistic systems assuming statistical term independence or term dependence. Examines the performance of models failing to meet statistical and other assumptions

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Abstract

State of the art review of the mechanisms of feedback in information retrieval (IR) in terms of feedback concepts and models in cybernetics and social sciences. Critically evaluates feedback research based on the traditional IR models and comparing the different approaches to automatic relevance feedback techniques, and feedback research within the framework of interactive IR models. Calls for an extension of the concept of feedback beyond relevance feedback to interactive feedback. Cites specific examples of feedback models used within IR research and presents 6 challenges to future research

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Abstract

Answers to questions concerning individual library decisions to adopt classification systems are important in understanding the efffectiveness of libraries but are difficult to provide. Measures of classification system performance are discussed, as are different methodologies that may be used to seek answers, ranging from formal or philosophical models to quantitative experimental techniques and qualitative methods

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Abstract

A text window is a group of words appearing in contiguous positions in text used to exploit a variety of lexical, syntactics, and semantic relationships without having to analyze the text explicitely for their structure. This supports the previously suggested idea that natural grouping of words are best treated as a unit of size 7 to 11 words, that is, plus or minus 3 to 5 words. The text retrieval experiments varying the size of windows, both with full text and with stopwords removed, support these size ranges. The characteristcs of windows that best match terms in queries are examined in detail, revealing intersting differences between those for queries with good results and those for queries with poorer results. Queries with good results tend to contain morte content word phrase and few terms with high frequency of use in the database. Information retrieval systems may benefit from expanding thesaurus-style relationships or incorporating statistical dependencies for terms within these windows

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Abstract

The use of terms from natural and social science titles and abstracts is studied from the perspective of sublanguages and their specialized dictionaries. Explores different notions of sublanguage distinctiveness. Object methods for separating hard and soft sciences are suggested based on measures of sublanguage use, dictionary characteristics, and sublanguage distinctiveness. Abstracts were automatically classified with a high degree of accuracy by using a formula that condsiders the degree of uniqueness of terms in each sublanguage. This may prove useful for text filtering of information retrieval systems

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Abstract

Knowledge of window style, content, location, and grammatical structure may be used to classify documents as originating within a particular discipline or may be used to place a document on a theory vs. practice spectrum. Examines characteristics of phrases and text windows, including their number, location in documents, and grammatical construction, in addition to studying variations in these window characteristics across disciplines. Examines some of the linguistic regularities for individual disciplines, and suggests families of regularities that may provide helpful for the automatic classification of documents, as well as for information retrieval and filtering applications