Search (140 results, page 1 of 7)

  • × year_i:[1990 TO 2000}
  • × theme_ss:"Automatisches Indexieren"
  1. Kutschekmanesch, S.; Lutes, B.; Moelle, K.; Thiel, U.; Tzeras, K.: Automated multilingual indexing : a synthesis of rule-based and thesaurus-based methods (1998) 0.04 
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    Source
    Information und Märkte: 50. Deutscher Dokumentartag 1998, Kongreß der Deutschen Gesellschaft für Dokumentation e.V. (DGD), Rheinische Friedrich-Wilhelms-Universität Bonn, 22.-24. September 1998. Hrsg. von Marlies Ockenfeld u. Gerhard J. Mantwill
    Type
    a
  2. Tsareva, P.V.: Algoritmy dlya raspoznavaniya pozitivnykh i negativnykh vkhozdenii deskriptorov v tekst i protsedura avtomaticheskoi klassifikatsii tekstov (1999) 0.03 
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    Date
    1. 4.2002 10:22:41
    Type
    a
  3. Tsujii, J.-I.: Automatic acquisition of semantic collocation from corpora (1995) 0.03 
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    Abstract
    Proposes automatic linguistic knowledge acquisition from sublanguage corpora. The system combines existing linguistic knowledge and human intervention with corpus based techniques. The algorithm involves a gradual approximation which works to converge linguistic knowledge gradually towards desirable results. The 1st experiment revealed the characteristic of this algorithm and the others proved the effectiveness of this algorithm for a real corpus
    Date
    31. 7.1996 9:22:19
    Type
    a
  4. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.03 
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    Abstract
    AutoSlog is a system that addresses the knowledge engineering bottleneck for information extraction. AutoSlog automatically creates domain specific dictionaries for information extraction, given an appropriate training corpus. Describes experiments with AutoSlog in terrorism, joint ventures and microelectronics domains. Compares the performance of AutoSlog across the 3 domains, discusses the lessons learned and presents results from 2 experiments which demonstrate that novice users can generate effective dictionaries using AutoSlog
    Date
    6. 3.1997 16:22:15
    Type
    a
  5. Bordoni, L.; Pazienza, M.T.: Documents automatic indexing in an environmental domain (1997) 0.03 
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    Abstract
    Describes an application of Natural Language Processing (NLP) techniques, in HIRMA (Hypertextual Information Retrieval Managed by ARIOSTO), to the problem of document indexing by referring to a system which incorporates natural language processing techniques to determine the subject of the text of documents and to associate them with relevant semantic indexes. Describes briefly the overall system, details of its implementation on a corpus of scientific abstracts related to environmental topics and experimental evidence of the system's behaviour. Analyzes in detail an experiment designed to evaluate the system's retrieval ability in terms of recall and precision
    Source
    International forum on information and documentation. 22(1997) no.1, S.17-28
    Type
    a
  6. Wolfekuhler, M.R.; Punch, W.F.: Finding salient features for personal Web pages categories (1997) 0.03 
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    Abstract
    Examines techniques that discover features in sets of pre-categorized documents, such that similar documents can be found on the WWW. Examines techniques which will classifiy training examples with high accuracy, then explains why this is not necessarily useful. Describes a method for extracting word clusters from the raw document features. Results show that the clustering technique is successful in discovering word groups in personal Web pages which can be used to find similar information on the WWW
    Date
    1. 8.1996 22:08:06
    Footnote
    Contribution to a special issue of papers from the 6th International World Wide Web conference, held 7-11 Apr 1997, Santa Clara, California
    Type
    a
  7. Ward, M.L.: ¬The future of the human indexer (1996) 0.02 
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    Abstract
    Considers the principles of indexing and the intellectual skills involved in order to determine what automatic indexing systems would be required in order to supplant or complement the human indexer. Good indexing requires: considerable prior knowledge of the literature; judgement as to what to index and what depth to index; reading skills; abstracting skills; and classification skills, Illustrates these features with a detailed description of abstracting and indexing processes involved in generating entries for the mechanical engineering database POWERLINK. Briefly assesses the possibility of replacing human indexers with specialist indexing software, with particular reference to the Object Analyzer from the InTEXT automatic indexing system and using the criteria described for human indexers. At present, it is unlikely that the automatic indexer will replace the human indexer, but when more primary texts are available in electronic form, it may be a useful productivity tool for dealing with large quantities of low grade texts (should they be wanted in the database)
    Date
    9. 2.1997 18:44:22
    Type
    a
  8. Plaunt, C.; Norgard, B.A.: ¬An association-based method for automatic indexing with a controlled vocabulary (1998) 0.02 
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    Abstract
    In this article, we describe and test a two-stage algorithm based on a lexical collocation technique which maps from the lexical clues contained in a document representation into a controlled vocabulary list of subject headings. Using a collection of 4.626 INSPEC documents, we create a 'dictionary' of associations between the lexical items contained in the titles, authors, and abstracts, and controlled vocabulary subject headings assigned to those records by human indexers using a likelihood ratio statistic as the measure of association. In the deployment stage, we use the dictiony to predict which of the controlled vocabulary subject headings best describe new documents when they are presented to the system. Our evaluation of this algorithm, in which we compare the automatically assigned subject headings to the subject headings assigned to the test documents by human catalogers, shows that we can obtain results comparable to, and consistent with, human cataloging. In effect we have cast this as a classic partial match information retrieval problem. We consider the problem to be one of 'retrieving' (or assigning) the most probably 'relevant' (or correct) controlled vocabulary subject headings to a document based on the clues contained in that document
    Date
    11. 9.2000 19:53:22
    Type
    a
  9. Milstead, J.L.: Thesauri in a full-text world (1998) 0.02 
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    Abstract
    Despite early claims to the contemporary, thesauri continue to find use as access tools for information in the full-text environment. Their mode of use is changing, but this change actually represents an expansion rather than a contrdiction of their utility. Thesauri and similar vocabulary tools can complement full-text access by aiding users in focusing their searches, by supplementing the linguistic analysis of the text search engine, and even by serving as one of the tools used by the linguistic engine for its analysis. While human indexing contunues to be used for many databases, the trend is to increase the use of machine aids for this purpose. All machine-aided indexing (MAI) systems rely on thesauri as the basis for term selection. In the 21st century, the balance of effort between human and machine will change at both input and output, but thesauri will continue to play an important role for the foreseeable future
    Date
    22. 9.1997 19:16:05
    Type
    a
  10. Jones, K.P.: Natural-language processing and automatic indexing : a reply (1990) 0.00 
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  11. Losee, R.M.: ¬A Gray code based ordering for documents on shelves : classification for browsing and retrieval (1992) 0.00 
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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
    Type
    a
  12. Driscoll, J.R.; Rajala, D.A.; Shaffer, W.H.: ¬The operation and performance of an artificially intelligent keywording system (1991) 0.00 
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    Abstract
    Presents a new approach to text analysis for automating the key phrase indexing process, using artificial intelligence techniques. This mimics the behaviour of human experts by using a rule base consisting of insertion and deletion rules generated by subject-matter experts. The insertion rules are based on the idea that some phrases found in a text imply or trigger other phrases. The deletion rules apply to semantically ambiguous phrases where text presence alone does not determine appropriateness as a key phrase. The insertion and deletion rules are used to transform a list of found phrases to a list of key phrases for indexing a document. Statistical data are provided to demonstrate the performance of this expert rule based system
    Type
    a
  13. Renouf, A.: Making sense of text : automated approaches to meaning extraction (1993) 0.00 
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  14. Salton, G.; Allen, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine-readable data (1994) 0.00 
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  15. Silvester, J.P.: Computer supported indexing : a history and evaluation of NASA's MAI system (1998) 0.00 
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  16. Ferber, R.: Automated indexing with thesaurus descriptors : a co-occurence based approach to multilingual retrieval (1997) 0.00 
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    Abstract
    Indexing documents with descriptors from a multilingual thesaurus is an approach to multilingual information retrieval. However, manual indexing is expensive. Automazed indexing methods in general use terms found in the document. Thesaurus descriptors are complex terms that are often not used in documents or have specific meanings within the thesaurus; therefore most weighting schemes of automated indexing methods are not suited to select thesaurus descriptors. In this paper a linear associative system is described that uses similarity values extracted from a large corpus of manually indexed documents to construct a rank ordering of the descriptors for a given document title. The system is adaptive and has to be tuned with a training sample of records for the specific task. The system was tested on a corpus of some 80.000 bibliographic records. The results show a high variability with changing parameter values. This indicated that it is very important to empirically adapt the model to the specific situation it is used in. The overall median of the manually assigned descriptors in the automatically generated ranked list of all 3.631 descriptors is 14 for the set used to adapt the system and 11 for a test set not used in the optimization process. This result shows that the optimization is not a fitting to a specific training set but a real adaptation of the model to the setting
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    a
  17. Clavel, G.; Walther, F.; Walther, J.: Indexation automatique de fonds bibliotheconomiques (1993) 0.00 
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    Abstract
    A discussion of developments to date in the field of computerized indexing, based on presentations given at a seminar held at the Institute of Policy Studies in Paris in Nov 91. The methods tested so far, based on a linguistic approach, whether using natural language or special thesauri, encounter the same central problem - they are only successful when applied to collections of similar types of documents covering very specific subject areas. Despite this, the search for some sort of universal indexing metalanguage continues. In the end, computerized indexing works best when used in conjunction with manual indexing - ideally in the hands of a trained library science professional, who can extract the maximum value from a collection of documents for a particular user population
    Type
    a
  18. Hlava, M.M.K.: Machine-Aided Indexing (MAI) in a multilingual environemt (1992) 0.00 
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    Abstract
    The Machine-Aided Indexing (MAI) program, developed by Access Innovations, Inc., is a semantic based, Boolean statement, rule interpreting application designed to operate in a multilingual environment. Use of MAI across several languages with controlled vocabularies for each language provides a consistency in indexing not available through any other mechanism
    Type
    a
  19. Cunningham, P.; Veale, T.; Conway, A.: Knowledge acquisition for concept indexing in document retrieval (1992) 0.00 
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    Abstract
    Describes TWIG, a system for knowledge acquisition from text for use in an intelligent document database system. Documents are scanned into the system and converted into a hypertext thus providing a richer environment for browsing and retrieval. The knowledge acquisition phase is blackboard based with the text analysis expertise partitioned into agents that communicate through the blackboard
    Type
    a
  20. Dow Jones unveils knowledge indexing system (1997) 0.00 
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    Abstract
    Dow Jones Interactive Publishing has developed a sophisticated automatic knowledge indexing system that will allow searchers of the Dow Jones News / Retrieval service to get highly targeted results from a search in the service's Publications Library. Instead of relying on a thesaurus of company names, the new system uses a combination of that basic algorithm plus unique rules based on the editorial styles of individual publications in the Library. Dow Jones have also announced its acceptance of the definitions of 'selected full text' and 'full text' from Bibliodata's Fulltext Sources Online directory
    Type
    a

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