Search (16 results, page 1 of 1)

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Abstract

The attempt to computerize a process, such as indexing, abstracting, classifying, or retrieving information, begins with an analysis of the process into its intellectual and nonintellectual components. That part of the process which is amenable to computerization is mechanical or algorithmic. What is not is intellectual or creative and requires human intervention. Gerard Salton has been an innovator, experimenter, and promoter in the area of mechanized information systems since the early 1960s. He has been particularly ingenious at analyzing the process of information retrieval into its algorithmic components. He received a doctorate in applied mathematics from Harvard University before moving to the computer science department at Cornell, where he developed a prototype automatic retrieval system called SMART. Working with this system he and his students contributed for over a decade to our theoretical understanding of the retrieval process. On a more practical level, they have contributed design criteria for operating retrieval systems. The following selection presents one of the early descriptions of the SMART system; it is valuable as it shows the direction automatic retrieval methods were to take beyond simple word-matching techniques. These include various word normalization techniques to improve recall, for instance, the separation of words into stems and affixes; the correlation and clustering, using statistical association measures, of related terms; and the identification, using a concept thesaurus, of synonymous, broader, narrower, and sibling terms. They include, as weIl, techniques, both linguistic and statistical, to deal with the thorny problem of how to automatically extract from texts index terms that consist of more than one word. They include weighting techniques and various documentrequest matching algorithms. Significant among the latter are those which produce a retrieval output of citations ranked in relevante order. During the 1970s, Salton and his students went an to further refine these various techniques, particularly the weighting and statistical association measures. Many of their early innovations seem commonplace today. Some of their later techniques are still ahead of their time and await technological developments for implementation. The particular focus of the selection that follows is an the evaluation of a particular component of the SMART system, a multilingual thesaurus. By mapping English language expressions and their German equivalents to a common concept number, the thesaurus permitted the automatic processing of German language documents against English language queries and vice versa. The results of the evaluation, as it turned out, were somewhat inconclusive. However, this SMART experiment suggested in a bold and optimistic way how one might proceed to answer such complex questions as What is meant by retrieval language compatability? How it is to be achieved, and how evaluated?

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Footnote

Bezugnahme auf: Blair, D.C.: An evaluation of retrieval effectiveness for a full-text document-retrieval system. Comm. ACM 28(1985) S.280-299. - Vgl. auch: Blair, D.C.: Full text retrieval ... Int. Class. 13(1986) S.18-23; Blair, D.C., M.E. Maron: full-text information retrieval ... Inf. Proc. Man. 26(1990) S.437-447.

Source

Communications of the Association for Computing Machinery. 29(1986), S.648-656

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Date

16. 8.1998 12:51:22

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Abstract

Über eine Schnittstelle zwischen Programmen zur automatischen Indexierung (PRIMUS-IDX) und zur maschinellen Thesaurusverwaltung (INDEX) sollen große Textmengen schnell, kostengünstig und konsistent erschlossen und verbesserte Recherchemöglichkeiten geschaffen werden. Zielvorstellung ist ein Verfahren, das auf PCs ablauffähig ist und speziell deutschsprachige Texte bearbeiten kann

Object

INDEX

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Abstract

Report of a study comparing 2 methods of indexing: LEXINET, a computerised system for indexing titles and summaries only; and manual indexing of full texts, using the thesaurus developed by French Electricity (EDF). Both systems were applied to a collection of approximately 2.000 documents on artifical intelligence from the EDF data base. The results were then analysed to compare quantitative performance (number and range of terms) and qualitative performance (ambiguity of terms, specificity, variability, consistency). Overall, neither system proved ideal: LEXINET was deficient as regards lack of accessibility and excessive ambiguity; while the manual system gave rise to an over-wide variation of terms. The ideal system would appear to be a combination of automatic and manual systems, on the evidence produced here.

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Abstract

This article describes a natural-language text-processing system designed as an automatic aid to subject indexing at BIOSIS. The intellectual procedure the system should model is a deep indexing with a controlled vocabulary of biological concepts - Concept Headings (CHs). On the average, ten CHs are assigned to each article by BIOSIS indexers. The automatic procedure consists of two stages: (1) translation of natural-language biological titles into title-semantic representations which are in the constructed formalized language of Concept Primitives, and (2) translation of the latter representations into the language of CHs. The first stage is performed by matching the titles agianst the system's Semantic Vocabulary (SV). The SV currently contains approximately 15.000 biological natural-language terms and their translations in the language of Concept Primitives. Tor the ambiguous terms, the SV contains the algorithmical rules of term disambiguation, ruels based on semantic analysis of the contexts. The second stage of the automatic procedure is performed by matching the title representations against the CH definitions, formulated as Boolean search strategies in the language of Concept Primitives. Three experiments performed with the system and their results are decribed. The most typical problems the system encounters, the problems of lexical and situational ambiguities, are discussed. The disambiguation techniques employed are described and demonstrated in many examples

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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.

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Abstract

Seit 1985 wird das automatische Indexierungssystem AIR/PHYS in der Inputproduktion der Physik-Datenbank PHYS des Fachinformationszentrums Karlsruhe angewandt. Das AIR/PHYS-System teilt englischsprachigen Referatetexten Deskriptoren aus einem vorgeschriebenen Vokabular zu. In der vorliegenden Arbeit werden der zugrundeliegende fehlertolerierende Ansatz, der Aufbau des Systems und die wichtigsten Verfahren zur Entwicklung eines großen Indexierungswörterbuches beschrieben. Ferner werden Probleme der Anwendung und Weiterentwicklung des Systems behandelt

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Abstract

Es wird zunächst ein "ideales Information-Retrieval-System" beschrieben und diskutiert. Als Kernproblem für -selbst bescheidene - Entwicklungen in die dadurch aufgezeigte Richtung wird das "Verstehen" von Texten durch den Computer angesehen, wobei je nach der Aufgabenstellung einer Systemkomponente stets nur ein partielles Verstehen erforderlich ist. Ein relativ einfaches, aber keineswegs triviales Beispiel dieser Art ist die automatische Indexierung von Referatetexten bei vorgegebenen Deskriptorensystem. Von diesem Problem werden Ansätze, Ergebnisse und Erfahrungen mitgeteilt. Darauf aufbauend werden weitere Forschungsrichtungen und Entwicklungsmöglichkeiten mitgeteilt

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Abstract

CONDOR ist ein Modell eines modularen, integrierten DB-/IR-Systems, mit dem sowohl strukturierte als auch unstrukturierte Daten (Textdaten) verarbeiet werden können. Die abzuspeichernden Informationen werden weitgehend automatich erschlossen. Da ein breiter Benutzerkreis Zugang zum System haben soll, sind verschiedene Dialogformen (Kommando, natürlichsprachlich, Formular, Menü) implementiert. Es wird versucht, sie in einer systematischen Oberflächengestaltung des Systems zusammenzuführen, um eine möglichst einfache Bedienung für den einzelnen Benutzer bei hoher Nutzungsflexibilität des Systems zu erreichen

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Abstract

The selection that follows was chosen as it represents "a very early paper an the possibilities allowed by computers an documentation." In the early 1960s computers were being used to provide simple automatic indexing systems wherein keywords were extracted from documents. The problem with such systems was that they lacked vocabulary control, thus documents related in subject matter were not always collocated in retrieval. To improve retrieval by improving recall is the raison d'être of vocabulary control tools such as classifications and thesauri. The question arose whether it was possible by automatic means to construct classes of terms, which when substituted, one for another, could be used to improve retrieval performance? One of the first theoretical approaches to this question was initiated by R. M. Needham and Karen Sparck Jones at the Cambridge Language Research Institute in England.t The question was later pursued using experimental methodologies by Sparck Jones, who, as a Senior Research Associate in the Computer Laboratory at the University of Cambridge, has devoted her life's work to research in information retrieval and automatic naturai language processing. Based an the principles of numerical taxonomy, automatic classification techniques start from the premise that two objects are similar to the degree that they share attributes in common. When these two objects are keywords, their similarity is measured in terms of the number of documents they index in common. Step 1 in automatic classification is to compute mathematically the degree to which two terms are similar. Step 2 is to group together those terms that are "most similar" to each other, forming equivalence classes of intersubstitutable terms. The technique for forming such classes varies and is the factor that characteristically distinguishes different approaches to automatic classification. The technique used by Needham and Sparck Jones, that of clumping, is described in the selection that follows. Questions that must be asked are whether the use of automatically generated classes really does improve retrieval performance and whether there is a true eco nomic advantage in substituting mechanical for manual labor. Several years after her work with clumping, Sparck Jones was to observe that while it was not wholly satisfactory in itself, it was valuable in that it stimulated research into automatic classification. To this it might be added that it was valuable in that it introduced to libraryl information science the methods of numerical taxonomy, thus stimulating us to think again about the fundamental nature and purpose of classification. In this connection it might be useful to review how automatically derived classes differ from those of manually constructed classifications: 1) the manner of their derivation is purely a posteriori, the ultimate operationalization of the principle of literary warrant; 2) the relationship between members forming such classes is essentially statistical; the members of a given class are similar to each other not because they possess the class-defining characteristic but by virtue of sharing a family resemblance; and finally, 3) automatically derived classes are not related meaningfully one to another, that is, they are not ordered in traditional hierarchical and precedence relationships.

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Source

Information processing and management. 22(1986) no.6, S.465-476

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Date

20.10.2000 12:22:23

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Date

14. 6.2015 22:12:44

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Date

14. 6.2015 22:12:56

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Date

14. 3.1996 13:22:21