Search (17 results, page 1 of 1)

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Abstract

The model of operationalist and conceptualist searching styles describes searching behavior of experienced online searchers. It is based on the systematic observation of five experienced online searchers doing their regular, job-related searches, and on the analysis of 10 to 13 searches conducted by each of them. Operationalist searchers aim at optimal strategies to achieve precise retrieval; they use a large range of system capabilities in their interaction. They preserve the specific meaning of the request, and the aim of their interactions is an answer set representing the request precisely. Conceptualist searchers analyze a request by seeking to fit it into a faceted structure. They first enter the facet that represents the most important aspect of the request. Their search is then centered on retrieving subsets from this primary set by introducing additional facets. In contrast to the operationalists, they are primarily concerned with recall. During the interaction they preserve the faceted structure, but may change the specific meaning of the request. Although not comprehensive, the model aids in recognizing special and individual characteristics of searching behavior which provide explanations of previous research and guidelines for further investigations into the search process

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Abstract

Describes information system and its various components-index file construstion, query formulation and searching. Discusses an indexing system, and brings out the need for its evaluation. Explains the concept of the efficiency of indexing systems and discusses factors which control this efficiency. Gives criteria for evaluation. Discusses recall and precision ratios, as also noise ratio, novelty ratio, and exhaustivity and specificity and the impact of each on the efficiency of indexing system. Mention also various steps for evaluation.

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Date

20.10.2000 12:22:23

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Abstract

A landmark event in the twentieth-century development of subject analysis theory was a retrieval experiment, begun in 1957, by Cyril Cleverdon, Librarian of the Cranfield Institute of Technology. For this work he received the Professional Award of the Special Libraries Association in 1962 and the Award of Merit of the American Society for Information Science in 1970. The objective of the experiment, called Cranfield I, was to test the ability of four indexing systems-UDC, Facet, Uniterm, and Alphabetic-Subject Headings-to retrieve material responsive to questions addressed to a collection of documents. The experiment was ambitious in scale, consisting of eighteen thousand documents and twelve hundred questions. Prior to Cranfield I, the question of what constitutes good indexing was approached subjectively and reference was made to assumptions in the form of principles that should be observed or user needs that should be met. Cranfield I was the first large-scale effort to use objective criteria for determining the parameters of good indexing. Its creative impetus was the definition of user satisfaction in terms of precision and recall. Out of the experiment emerged the definition of recall as the percentage of relevant documents retrieved and precision as the percentage of retrieved documents that were relevant. Operationalizing the concept of user satisfaction, that is, making it measurable, meant that it could be studied empirically and manipulated as a variable in mathematical equations. Much has been made of the fact that the experimental methodology of Cranfield I was seriously flawed. This is unfortunate as it tends to diminish Cleverdon's contribu tion, which was not methodological-such contributions can be left to benchmark researchers-but rather creative: the introduction of a new paradigm, one that proved to be eminently productive. The criticism leveled at the methodological shortcomings of Cranfield I underscored the need for more precise definitions of the variables involved in information retrieval. Particularly important was the need for a definition of the dependent variable index language. Like the definitions of precision and recall, that of index language provided a new way of looking at the indexing process. It was a re-visioning that stimulated research activity and led not only to a better understanding of indexing but also the design of better retrieval systems." Cranfield I was followed by Cranfield II. While Cranfield I was a wholesale comparison of four indexing "systems," Cranfield II aimed to single out various individual factors in index languages, called "indexing devices," and to measure how variations in these affected retrieval performance. The following selection represents the thinking at Cranfield midway between these two notable retrieval experiments.

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Abstract

A retrievalsystem was built to find individuals with appropriate expertise within a large research establishment on the basis of their authored documents. The expert-locating system uses a new method for automatic indexing and retrieval based on singular value decomposition, a matrix decomposition technique related to the factor analysis. Organizational groups, represented by the documents they write, and the terms contained in these documents, are fit simultaneously into a 100-dimensional "semantic" space. User queries are positioned in the semantic space, and the most similar groups are returned to the user. Here we compared the standard vector-space model with this new technique and found that combining the two methods improved performance over either alone. We also examined the effects of various experimental variables on the system`s retrieval accuracy. In particular, the effects of: term weighting functions in the semantic space construction and in query construction, suffix stripping, and using lexical units larger than a a single word were studied.

Object

Latent Semantic Indexing

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Abstract

This study systematically compares retrievals on 11 topics across five well-known data bases, with MEDLINE's subject indexing as a focus. Each topic was posed by a researcher in the medical behavioral sciences. Each was searches in MEDLINE, EXCERPTA MEDICA, and PSYCHINFO, which permit descriptor searches, and in SCISEARCH and SOCIAL SCISEARCH, which express topics through cited references. Searches on each topic were made with (1) descriptors, (2) cited references, and (3) natural language (a capabiblity common to all five data bases). The researchers who posed the topics judged the results. In every case, the set of records judged relevant was used to to calculate recall, precision, and novelty ratios. Overall, MEDLINE had the highest recall percentage (37%), followed by SSCI (31%). All searches resulted in high precision ratios; novelty ratios of data bases and searches varied widely. Differences in record format among data bases affected the success of the natural language retrievals. Some 445 documents judged relevant were not retrieved from MEDLINE using its descriptors; they were found in MEDLINE through natural language or in an alternative data base. An analysis was performed to examine possible faults in MEDLINE subject indexing as the reason for their nonretrieval. However, no patterns of indexing failure could be seen in those documents subsequently found in MEDLINE through known-item searches. Documents not found in MEDLINE primarily represent failures of coverage - articles were from nonindexed or selectively indexed journals

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Abstract

This article reports on a comparison of the postcoordinate retrieval effectiveness of two indexing languages: LCSH and PRECIS. The effect of augmenting each with title words was also studies. The database for the study was over 15.000 UK MARC records. Users returned 5.326 relevant judgements for citations retrieved for 61 SDI profiles, representing a wide variety of subjects. Results are reported in terms of precision and relative recall. Pure/applied sciences data and social science data were analyzed separately. Cochran's significance tests for ratios were used to interpret the findings. Recall emerged as the more important measure discriminating the behavior of the two languages. Addition of title words was found to improve recall of both indexing languages significantly. A direct relationship was observed between recall and exhaustivity. For the social sciences searches, recalls from PRECIS alone and from PRECIS with title words were significantly higher than those from LCSH alone and from LCSH with title words, respectively. Corresponding comparisons for the pure/applied sciences searches revealed no significant differences

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Pages

S.22-25

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Abstract

F. W. Lancaster is known for his writing an the state of the art in librarylinformation science. His skill in identifying significant contributions and synthesizing literature in fields as diverse as online systems, vocabulary control, measurement and evaluation, and the paperless society have earned him esteem as a chronicler of information science. Equally deserving of repute is his own contribution to research in the discipline-his evaluation of the MEDLARS operating system. The MEDLARS study is notable for several reasons. It was the first large-scale application of retrieval experiment methodology to the evaluation of an actual operating system. As such, problems had to be faced that do not arise in laboratory-like conditions. One example is the problem of recall: how to determine, for a very large and dynamic database, the number of documents relevant to a given search request. By solving this problem and others attendant upon transferring an experimental methodology to the real world, Lancaster created a constructive procedure that could be used to improve the design and functioning of retrieval systems. The MEDLARS study is notable also for its contribution to our understanding of what constitutes a good index language and good indexing. The ideal retrieval system would be one that retrieves all and only relevant documents. The failures that occur in real operating systems, when a relevant document is not retrieved (a recall failure) or an irrelevant document is retrieved (a precision failure), can be analysed to assess the impact of various factors an the performance of the system. This is exactly what Lancaster did. He found both the MEDLARS indexing and the McSH index language to be significant factors affecting retrieval performance. The indexing, primarily because it was insufficiently exhaustive, explained a large number of recall failures. The index language, largely because of its insufficient specificity, accounted for a large number of precision failures. The purpose of identifying factors responsible for a system's failures is ultimately to improve the system. Unlike many user studies, the MEDLARS evaluation yielded recommendations that were eventually implemented.* Indexing exhaustivity was increased and the McSH index language was enriched with more specific terms and a larger entry vocabulary.

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Theme

Citation indexing

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Theme

Citation indexing

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Date

14. 3.1996 13:22:21

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Date

9. 1.1996 10:22:31