Search (18 results, page 1 of 1)

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Theme

Preserved Context Index System (PRECIS)

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Preserved Context Index System (PRECIS)

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Preserved Context Index System (PRECIS)

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Source

Catalogue and index. 14(1969), April, S.11-13

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Preserved Context Index System (PRECIS)

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Preserved Context Index System (PRECIS)

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Abstract

Derek Austin's PRECIS, or PREserved Context Index System, was one of the most sophisticated indexing systems ever developed for text. Austin noted that there are many ways that the meaning of index entries may be misread and misunderstood, if the original context from which the concept came was not present. Drawing on a good understanding of linguistics, Austin and his colleagues developed a highly rigorous, yet not impossibly difficult, set of principles to use in indexing with the help of PRECIS. The system was particularly valuable for generating multiple word orderings of entries that still retained their proper contextual meaning. Much of the need for this feature was obviated by the development of powerful full text searching in information systems, however, and PRECIS receded from view in information science. The final sections of the article that address the details of coding and indexing management are deleted from the text below.

Theme

Preserved Context Index System (PRECIS)

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Abstract

The present paper is offered as the first of a series of articles in which PRECIS will be reviewed as a potential multi-lingual system, having in mind the obvious need, notably in a European context, for standard and language independent methods of subject analysis and document description. This first paper outlines the origins of PRECIS, and considers its use in English language indexing. A second paper will deal in general terms with the syntactical model which is used for producing PRECIS input strings and index entries. Later papers will then review the application of this model to indexing in, firstly, the Germanic languages (e.g. German and Danish), and, secondly, the Romance languages, illustrated by French.

Theme

Preserved Context Index System (PRECIS)

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Abstract

Probably the most interesting, original and controversial indexing system of the 1970s has been PRECIS (the Preserved Context Index System) developed by Derek Austin. It attracted such attention that the short courses given at the British National Bibliography offices in London -initiated as a means of teaching the staff of BNB who did subject indexing-were repeated all over the world, in the form of three-day or three-week sessions. A few fortunate non-BNB indexers were taken into London classes, taught by Austin and Digger, and had the advantage of a week's work with the regular staff theresomething that could not be done elsewhere. In addition, descriptions of the system were published from time to time as progress was made an the system itself and clarification or updating were needed. This article has been selected as an example of clarification. A second edition of the original 1974 Manual describing PRECIS appeared in early 1984, so the system may now be regarded as fully mature. PRECIS, which is the culmination of Austin's research for the Classification Research Group plus considerable additional input from the science of linguistics, is a highly sophisticated indexing system, deliberately created to be used with a computer. The motive behind it was that the indexer should do the brain work and the computer the nitty-gritty work of manipulating index terms. Chosen combinations and permutations are used as head words under strict rules to ensure that the context which defines the specific meaning of these words is always present, thus avoiding the ambiguity problem in indexing. The system also has classifi ation features, in part drawn from the Group's long interest in faceted classification. The actual index produces a large number of index entries, each carrying with it the basic context of the item being described. A weIl-defined terminology, in addition to contextdependency, is a feature of PRECIS. Role operators define the types of terms used and control the manipulations of the terms, which are originally entered as a string. The indexer is able to analyze and reduce to string form any document, process, or material that can be described in words, making it ideal for most audiovisual material.
The system itself has two major aspects: - a syntactic approach in which relationships between terms are identified and tagged. Suitable algorithms for manipulation produce a very thorough index in which each head word is kept in context. - a semantic approach in which terminology is weIl defined by ensuring that the relationships of equivalence, hierarchy, and association are clearly established. Charts of relationships are created by inductive means (i.e., from the ground up). A special code for computer manipulation is used with each line of the string. This translates the string format into one that, with specially derived algorithms, can be used by the computer, to produce the actual index headings with all parts rotated by following the algorithm. The PRECIS system has been translated and utilized for languages other than English, notably French, German, and Danish. Modifications needed for idiosyncracies of each language have been made. Some, such as those modifications needed for German, have turned out to have usefulness with English as weIl. All in all, the system has such advanced indexing concepts that it provides a kind of indexing not possible before the computer came into common usage. Austin, who joined the staff of the British National Bibliography in 1963, is currently a member of the British Library staff. His early work included a NATO-sponsored research assignment for the Classification Research Group (q.v.). This, in turn, led to the development of PRECIS.

Theme

Preserved Context Index System (PRECIS)

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Preserved Context Index System (PRECIS)

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Preserved Context Index System (PRECIS)

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Abstract

Imagine a library containing 25 billion documents but with no centralized organization and no librarians. In addition, anyone may add a document at any time without telling anyone. You may feel sure that one of the documents contained in the collection has a piece of information that is vitally important to you, and, being impatient like most of us, you'd like to find it in a matter of seconds. How would you go about doing it? Posed in this way, the problem seems impossible. Yet this description is not too different from the World Wide Web, a huge, highly-disorganized collection of documents in many different formats. Of course, we're all familiar with search engines (perhaps you found this article using one) so we know that there is a solution. This article will describe Google's PageRank algorithm and how it returns pages from the web's collection of 25 billion documents that match search criteria so well that "google" has become a widely used verb. Most search engines, including Google, continually run an army of computer programs that retrieve pages from the web, index the words in each document, and store this information in an efficient format. Each time a user asks for a web search using a search phrase, such as "search engine," the search engine determines all the pages on the web that contains the words in the search phrase. (Perhaps additional information such as the distance between the words "search" and "engine" will be noted as well.) Here is the problem: Google now claims to index 25 billion pages. Roughly 95% of the text in web pages is composed from a mere 10,000 words. This means that, for most searches, there will be a huge number of pages containing the words in the search phrase. What is needed is a means of ranking the importance of the pages that fit the search criteria so that the pages can be sorted with the most important pages at the top of the list. One way to determine the importance of pages is to use a human-generated ranking. For instance, you may have seen pages that consist mainly of a large number of links to other resources in a particular area of interest. Assuming the person maintaining this page is reliable, the pages referenced are likely to be useful. Of course, the list may quickly fall out of date, and the person maintaining the list may miss some important pages, either unintentionally or as a result of an unstated bias. Google's PageRank algorithm assesses the importance of web pages without human evaluation of the content. In fact, Google feels that the value of its service is largely in its ability to provide unbiased results to search queries; Google claims, "the heart of our software is PageRank." As we'll see, the trick is to ask the web itself to rank the importance of pages.