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  • × theme_ss:"Automatisches Indexieren"
  1. Riloff, E.: ¬An empirical study of automated dictionary construction for information extraction in three domains (1996) 0.09 
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    Date
    6. 3.1997 16:22:15
    Source
    Artificial intelligence. 85(1996) nos.1/2, S.101-134
  2. Oliver, C.: Leveraging KOS to extend our reach with automated processes (2021) 0.04 
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    Abstract
    This article provides a conclusion to the special issue on Artificial Intelligence (AI) and Automated Processes for Subject Access. The authors who contributed to this special issue have provoked interesting questions as well as bringing attention to important issues. This concluding article looks at common themes and highlights some of the questions raised.
    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
  3. Wolfram Language erkennt Bilder (2015) 0.03 
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    Content
    Vgl.: http://www.imageidentify.com. Eine ausführlichere Erklärung der Funktionsweise und Hintergründe findet sich in Stephen Wolframs Blog-Eintrag: "Wolfram Language Artificial Intelligence: The Image Identification Project" unter: http://blog.stephenwolfram.com/2015/05/wolfram-language-artificial-intelligence-the-image-identification-project/. Vgl. auch: https://news.ycombinator.com/item?id=8621658.
  4. Asula, M.; Makke, J.; Freienthal, L.; Kuulmets, H.-A.; Sirel, R.: Kratt: developing an automatic subject indexing tool for the National Library of Estonia : how to transfer metadata information among work cluster members (2021) 0.03 
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    Abstract
    Manual subject indexing in libraries is a time-consuming and costly process and the quality of the assigned subjects is affected by the cataloger's knowledge on the specific topics contained in the book. Trying to solve these issues, we exploited the opportunities arising from artificial intelligence to develop Kratt: a prototype of an automatic subject indexing tool. Kratt is able to subject index a book independent of its extent and genre with a set of keywords present in the Estonian Subject Thesaurus. It takes Kratt approximately one minute to subject index a book, outperforming humans 10-15 times. Although the resulting keywords were not considered satisfactory by the catalogers, the ratings of a small sample of regular library users showed more promise. We also argue that the results can be enhanced by including a bigger corpus for training the model and applying more careful preprocessing techniques.
    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
  5. Schuegraf, E.J.; Bommel, M.F.van: ¬An automatic document indexing system based on cooperating expert systems : design and development (1993) 0.03 
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    Abstract
    Discusses the design of an automatic indexing system based on two cooperating expert systems and the investigation related to its development. The design combines statistical and artificial intelligence techniques. Examines choice of content indicators, the effect of stemming and the identification of characteristic vocabularies for given subject areas. Presents experimental results. Discusses the application of machine learning algorithms to the identification of vocabularies
  6. Prasad, A.R.D.: PROMETHEUS: an automatic indexing system (1996) 0.03 
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    Abstract
    An automatic indexing system using the tools and techniques of artificial intelligence is described. The paper presents the various components of the system like the parser, grammar formalism, lexicon, and the frame based knowledge representation for semantic representation. The semantic representation is based on the Ranganathan school of thought, especially that of Deep Structure of Subject Indexing Languages enunciated by Bhattacharyya. It is attempted to demonstrate the various stepts in indexing by providing an illustration
  7. Pritchard-Schoch, T.: Natural language comes of age (1993) 0.03 
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    Abstract
    Discusses natural languages and the natural language implementations of Westlaw's full-text legal documents, Westlaw Is Natural. Natural language is not aritificial intelligence but a hybrid of linguistics, mathematics and statistics. Provides 3 classes of retrieval models. Explains how Westlaw processes an English query. Assesses WIN. Covers WIN enhancements; the natural language features of Congressional Quarterly's Washington Alert using a document for a query; the personal librarian front end search software and Dowquest from Dow Jones news/retrieval. Conmsiders whether natural language encourages fuzzy thinking and whether Boolean logic will still be needed
  8. Driscoll, J.R.; Rajala, D.A.; Shaffer, W.H.: ¬The operation and performance of an artificially intelligent keywording system (1991) 0.03 
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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
  9. Taylor, S.L.: Integrating natural language understanding with document structure analysis (1994) 0.03 
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    Source
    Artificial intelligence review. 8(1994) nos.2/3, S.255-276
  10. Chartron, G.; Dalbin, S.; Monteil, M.-G.; Verillon, M.: Indexation manuelle et indexation automatique : dépasser les oppositions (1989) 0.03 
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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.
  11. Moulaison-Sandy, H.; Adkins, D.; Bossaller, J.; Cho, H.: ¬An automated approach to describing fiction : a methodology to use book reviews to identify affect (2021) 0.03 
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    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
  12. Golub, K.: Automated subject indexing : an overview (2021) 0.03 
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    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
  13. Chou, C.; Chu, T.: ¬An analysis of BERT (NLP) for assisted subject indexing for Project Gutenberg (2022) 0.03 
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    Abstract
    In light of AI (Artificial Intelligence) and NLP (Natural language processing) technologies, this article examines the feasibility of using AI/NLP models to enhance the subject indexing of digital resources. While BERT (Bidirectional Encoder Representations from Transformers) models are widely used in scholarly communities, the authors assess whether BERT models can be used in machine-assisted indexing in the Project Gutenberg collection, through suggesting Library of Congress subject headings filtered by certain Library of Congress Classification subclass labels. The findings of this study are informative for further research on BERT models to assist with automatic subject indexing for digital library collections.
  14. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.03 
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  15. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.02 
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    Date
    20.10.2000 12:22:23
  16. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.02 
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  17. Advances in intelligent retrieval: Proc. of a conference ... Wadham College, Oxford, 16.-17.4.1985 (1986) 0.02 
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    Content
    Enthält die Beiträge: ADDIS, T.: Extended relational analysis: a design approach to knowledge-based systems; PARKINSON, D.: Supercomputers and non-numeric processing; McGREGOR, D.R. u. J.R. MALONE: An architectural approach to advances in information retrieval; ALLEN, M.J. u. O.S. HARRISON: Word processing and information retrieval: some practical problems; MURTAGH, F.: Clustering and nearest neighborhood searching; ENSER, P.G.B.: Experimenting with the automatic classification of books; TESKEY, N. u. Z. RAZAK: An analysis of ranking for free text retrieval systems; ZARRI, G.P.: Interactive information retrieval: an artificial intelligence approach to deal with biographical data; HANCOX, P. u. F. SMITH: A case system processor for the PRECIS indexing language; ROUAULT, J.: Linguistic methods in information retrieval systems; ARAGON-RAMIREZ, V. u. C.D. PAICE: Design of a system for the online elucidation of natural language search statements; BROOKS, H.M., P.J. DANIELS u. N.J. BELKIN: Problem descriptions and user models: developing an intelligent interface for document retrieval systems; BLACK, W.J., P. HARGREAVES u. P.B. MAYES: HEADS: a cataloguing advisory system; BELL, D.A.: An architecture for integrating data, knowledge, and information bases
  18. Lowe, D.B.; Dollinger, I.; Koster, T.; Herbert, B.E.: Text mining for type of research classification (2021) 0.02 
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    Footnote
    Teil eines Themenheftes: Artificial intelligence (AI) and automated processes for subject sccess
  19. Fuhr, N.: Ranking-Experimente mit gewichteter Indexierung (1986) 0.02 
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    Date
    14. 6.2015 22:12:44
  20. Hauer, M.: Automatische Indexierung (2000) 0.02 
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              3.5018296 = idf(docFreq=3622, maxDocs=44218)
              0.09375 = fieldNorm(doc=5887)
      0.5 = coord(1/2)
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    Source
    Wissen in Aktion: Wege des Knowledge Managements. 22. Online-Tagung der DGI, Frankfurt am Main, 2.-4.5.2000. Proceedings. Hrsg.: R. Schmidt

Authors

Years

Languages

  • e 31
  • d 16
  • f 1
  • ru 1
  • More… Less…

Types

  • a 42
  • el 4
  • x 2
  • m 1
  • s 1
  • More… Less…

Themes