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  • × theme_ss:"Automatisches Indexieren"
  1. Hauer, M.: Automatische Indexierung (2000) 0.05
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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
  2. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.04
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    Date
    1. 2.2016 18:25:22
  3. Fox, C.: Lexical analysis and stoplists (1992) 0.03
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    Source
    Information retrieval: data structures and algorithms. Ed.: W.B. Frakes u. R. Baeza-Yates
  4. Ferber, R.: Automated indexing with thesaurus descriptors : a co-occurence based approach to multilingual retrieval (1997) 0.02
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    Source
    Research and advanced technology for digital libraries: First European Conference, ECDL'97, Pisa, Italy, September 1997, Proceedings. Ed.: C. Peters u. C. Thanos
  5. Goller, C.; Löning, J.; Will, T.; Wolff, W.: Automatic document classification : a thourough evaluation of various methods (2000) 0.02
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    Source
    Informationskompetenz - Basiskompetenz in der Informationsgesellschaft: Proceedings des 7. Internationalen Symposiums für Informationswissenschaft (ISI 2000), Hrsg.: G. Knorz u. R. Kuhlen
  6. Plaunt, C.; Norgard, B.A.: ¬An association-based method for automatic indexing with a controlled vocabulary (1998) 0.02
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    Date
    11. 9.2000 19:53:22
  7. Martins, A.L.; Souza, R.R.; Ribeiro de Mello, H.: ¬The use of noun phrases in information retrieval : proposing a mechanism for automatic classification (2014) 0.02
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    Abstract
    This paper presents a research on syntactic structures known as noun phrases (NP) being applied to increase the effectiveness and efficiency of the mechanisms for the document's classification. Our hypothesis is the fact that the NP can be used instead of single words as a semantic aggregator to reduce the number of words that will be used for the classification system without losing its semantic coverage, increasing its efficiency. The experiment divided the documents classification process in three phases: a) NP preprocessing b) system training; and c) classification experiments. In the first step, a corpus of digitalized texts was submitted to a natural language processing platform1 in which the part-of-speech tagging was done, and them PERL scripts pertaining to the PALAVRAS package were used to extract the Noun Phrases. The preprocessing also involved the tasks of a) removing NP low meaning pre-modifiers, as quantifiers; b) identification of synonyms and corresponding substitution for common hyperonyms; and c) stemming of the relevant words contained in the NP, for similitude checking with other NPs. The first tests with the resulting documents have demonstrated its effectiveness. We have compared the structural similarity of the documents before and after the whole pre-processing steps of phase one. The texts maintained the consistency with the original and have kept the readability. The second phase involves submitting the modified documents to a SVM algorithm to identify clusters and classify the documents. The classification rules are to be established using a machine learning approach. Finally, tests will be conducted to check the effectiveness of the whole process.
    Source
    Knowledge organization in the 21st century: between historical patterns and future prospects. Proceedings of the Thirteenth International ISKO Conference 19-22 May 2014, Kraków, Poland. Ed.: Wieslaw Babik
  8. Schneider, C.; Womser-Hacker, C.: Inhaltserschließungssysteme für Patenttexte : Test und Systemvergleich im Projekt PADOK (1986) 0.02
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  9. Voorhees, E.M.: Implementing agglomerative hierarchic clustering algorithms for use in document retrieval (1986) 0.02
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    Source
    Information processing and management. 22(1986) no.6, S.465-476
  10. Jones, K.P.: Natural-language processing and automatic indexing : a reply (1990) 0.02
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    Footnote
    Erwiderung auf: Korycinski, C. u. A.F. Newell
  11. Smith, P.J.; Normore, L.F.; Denning, R.; Johnson, W.P.: Computerized tools to support document analysis (1994) 0.02
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    Source
    Challenges in indexing electronic text and images. Ed.: R. Fidel et al
  12. Fuhr, N.; Niewelt, B.: ¬Ein Retrievaltest mit automatisch indexierten Dokumenten (1984) 0.01
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    Date
    20.10.2000 12:22:23
  13. Hlava, M.M.K.: Automatic indexing : comparing rule-based and statistics-based indexing systems (2005) 0.01
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    Source
    Information outlook. 9(2005) no.8, S.22-23
  14. Gibb, F.; Smart, G.: Knowledge-based indexing : the view from SIMPR (1991) 0.01
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    Source
    Libraries and expert systems. Ed. C. MacDonald et al
  15. Schwarz, C.: Komplexe Nominalgruppen als Indexierungseinheiten am Beispiel des Projekte CONDOR (1982) 0.01
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  16. Salton, G.; Allen, J.; Buckley, C.; Singhal, A.: Automatic analysis, theme generation, and summarization of machine-readable data (1994) 0.01
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  17. Schröther, C.: Automatische Indexierung, Kategorisierung und inhaltliche Erschließung von Textnachrichten (2003) 0.01
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  18. SIGIR'92 : Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1992) 0.01
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    Abstract
    The conference was organized by the Royal School of Librarianship in Copenhagen and was held in cooperation with AICA-GLIR (Italy), BCS-IRSG (UK), DD (Denmark), GI (Germany), INRIA (France). It had support from Apple Computer, Denmark. The volume contains the 32 papers and reports on the two panel sessions, moderated by W.B. Croft, and R. Kovetz, respectively
    Content
    HARMAN, D.: Relevance feedback revisited; AALBERSBERG, I.J.: Incremental relevance feedback; TAGUE-SUTCLIFFE, J.: Measuring the informativeness of a retrieval process; LEWIS, D.D.: An evaluation of phrasal and clustered representations on a text categorization task; BLOSSEVILLE, M.J., G. HÉBRAIL, M.G. MONTEIL u. N. PÉNOT: Automatic document classification: natural language processing, statistical analysis, and expert system techniques used together; MASAND, B., G. LINOFF u. D. WALTZ: Classifying news stories using memory based reasoning; KEEN, E.M.: Term position ranking: some new test results; CROUCH, C.J. u. B. YANG: Experiments in automatic statistical thesaurus construction; GREFENSTETTE, G.: Use of syntactic context to produce term association lists for text retrieval; ANICK, P.G. u. R.A. FLYNN: Versioning of full-text information retrieval system; BURKOWSKI, F.J.: Retrieval activities in a database consisting of heterogeneous collections; DEERWESTER, S.C., K. WACLENA u. M. LaMAR: A textual object management system; NIE, J.-Y.:Towards a probabilistic modal logic for semantic-based information retrieval; WANG, A.W., S.K.M. WONG u. Y.Y. YAO: An analysis of vector space models based on computational geometry; BARTELL, B.T., G.W. COTTRELL u. R.K. BELEW: Latent semantic indexing is an optimal special case of multidimensional scaling; GLAVITSCH, U. u. P. SCHÄUBLE: A system for retrieving speech documents; MARGULIS, E.L.: N-Poisson document modelling; HESS, M.: An incrementally extensible document retrieval system based on linguistics and logical principles; COOPER, W.S., F.C. GEY u. D.P. DABNEY: Probabilistic retrieval based on staged logistic regression; FUHR, N.: Integration of probabilistic fact and text retrieval; CROFT, B., L.A. SMITH u. H. TURTLE: A loosely-coupled integration of a text retrieval system and an object-oriented database system; DUMAIS, S.T. u. J. NIELSEN: Automating the assignement of submitted manuscripts to reviewers; GOST, M.A. u. M. MASOTTI: Design of an OPAC database to permit different subject searching accesses; ROBERTSON, A.M. u. P. WILLETT: Searching for historical word forms in a database of 17th century English text using spelling correction methods; FAX, E.A., Q.F. CHEN u. L.S. HEATH: A faster algorithm for constructing minimal perfect hash functions; MOFFAT, A. u. J. ZOBEL: Parameterised compression for sparse bitmaps; GRANDI, F., P. TIBERIO u. P. Zezula: Frame-sliced patitioned parallel signature files; ALLEN, B.: Cognitive differences in end user searching of a CD-ROM index; SONNENWALD, D.H.: Developing a theory to guide the process of designing information retrieval systems; CUTTING, D.R., J.O. PEDERSEN, D. KARGER, u. J.W. TUKEY: Scatter/ Gather: a cluster-based approach to browsing large document collections; CHALMERS, M. u. P. CHITSON: Bead: Explorations in information visualization; WILLIAMSON, C. u. B. SHNEIDERMAN: The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploring system
  19. Fuhr, N.: Probabilistisches Indexing and Retrieval (1988) 0.01
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  20. Kuhlen, R.: Morphologische Relationen durch Reduktionsalgorithmen (1974) 0.01
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