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  • × theme_ss:"Automatisches Indexieren"
  1. Stankovic, R. et al.: Indexing of textual databases based on lexical resources : a case study for Serbian (2016) 0.30
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    Date
    1. 2.2016 18:25:22
    Source
    Semantic keyword-based search on structured data sources: First COST Action IC1302 International KEYSTONE Conference, IKC 2015, Coimbra, Portugal, September 8-9, 2015. Revised Selected Papers. Eds.: J. Cardoso et al
  2. Smith, P.J.; Normore, L.F.; Denning, R.; Johnson, W.P.: Computerized tools to support document analysis (1994) 0.21
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    Source
    Challenges in indexing electronic text and images. Ed.: R. Fidel et al
  3. Silvester, J.P.; Genuardi, M.T.: Machine-aided indexing from the analysis of natural language text (1994) 0.19
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    Source
    Challenges in indexing electronic text and images. Ed.: R. Fidel et al
  4. Harman, D.: Automatic indexing (1994) 0.13
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    Source
    Challenges in indexing electronic text and images. Ed.: R. Fidel et al
  5. Gibb, F.; Smart, G.: Knowledge-based indexing : the view from SIMPR (1991) 0.11
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    Source
    Libraries and expert systems. Ed. C. MacDonald et al
  6. Renouf, A.: Making sense of text : automated approaches to meaning extraction (1993) 0.11
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    Source
    Online information 93: 17th International Online Meeting Proceedings, London, 7.-9.12.1993. Ed. by D.I. Raitt et al
  7. Chevallet, J.-P.; Bruandet, M.F.: Impact de l'utilisation de multi terms sur la qualité des résponses dùn système de recherche d'information a indexation automatique (1999) 0.09
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    Series
    Collection travaux et recherches; UL3
    Source
    Organisation des connaissances en vue de leur intégration dans les systèmes de représentation et de recherche d'information. Ed.: J. Maniez, et al
  8. Gaus, W.; Kaluscha, R.: Maschinelle inhaltliche Erschließung von Arztbriefen und Auswertung von Reha-Entlassungsberichten (2006) 0.08
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    Abstract
    Schon Hippokrates empfahl den Ärzten, Krankenakten zu führen. Heute ist die detaillierte Dokumentation eine Standespflicht der Ärzte [Gaus et al 1999]. Diese Dokumentationen medizinischer Behandlungen bergen einen riesigen und wertvollen Erfahrungsschatz. Informationen zu Therapien und Behandlungsergebnissen, die in Studien erst mühsam erhoben werden müssten, sind bereits in der Routinedokumentation wie Operations- und Entlassungsberichten oder Arztbriefen zahlreich vorhanden. Mit der Einführung der elektronischen Datenverarbeitung in der Medizin liegen diese Informationen seit einigen Jahren auch maschinenlesbar vor, so dass ein Haupthemmnis für die Nutzung dieser Dokumentationen, nämlich die mühsame manuelle Aufbereitung der Papierakten, entfällt. Während die formale Erschließung nach Patientenmerkmalen wie Name und Geburtsdatum von den Krankenhaus- bzw. Praxisinformationssystemen heutzutage gut gelöst ist, bleibt die inhaltliche Erschließung dieser Dokumentationen schwierig, da nur wenige Informationen in strukturierter oder intellektuell indexierter Form vorliegen [Leiner et al. 2003]. Auch wenn nach der Einführung der Fallpauschalen (diagnosis related groups, DRG) in den Krankenhäusern die Diagnosen nach ICD-10 verschlüsselt werden, besteht ein Großteil der Informationen weiterhin aus freiem Text, dessen computerbasierte Erschließung aufgrund der Komplexität menschlicher Sprache nicht trivial ist. Zu diesen medizinischen Texten gehören u.a. Gutachten, verbal beschriebene (Differential-) Diagnosen, vielfältige Untersuchungs- und Befundberichte, Visitenblätter, Operationsberichte und der Arztbrief bzw. Entlassungsbericht. Arztbrief und Entlassbericht dienen der Information des einweisenden oder weiterbehandelnden Arztes (z.B. Hausarzt) über das, was mit dem Patienten geschehen ist, und geben Empfehlungen zur Weiterbehandlung. Sie fassen eine (stationäre) Behandlung epikritisch - also nachdem die Krankheit überwunden ist, im Rückblick - zusammen und geben einen Überblick über Anamnese (Vorgeschichte), Beschwerden und Symptome, die eingesetzten diagnostischen Verfahren, die gestellte(n) Diagnose(n), Therapie, Verlauf, Komplikationen und das erzielte Ergebnis. Sie haben somit eine ähnliche Funktion wie das Abstract in der Literaturdokumentation, oft wird eine Kopie in der Krankenakte obenauf abgelegt. Zumindest in Universitätskliniken möchten wissenschaftlich arbeitende Ärzte auch unter inhaltlichen Gesichtspunkten auf die Krankenakten zugreifen können, z.B. die Krankenakten aller Patienten mit einer bestimmten Diagnose einsehen, exzerpieren und die exzerpierten Daten auswerten. Auch bei der Suche nach ähnlichen Fällen oder im Bereich der Aus- und Fortbildung hilft eine inhaltliche Erschließung weiter. So könnte etwa ein Assistenzarzt, der im Rahmen seiner Weiterbildung demnächst Sonografien des Kniegelenkes durchzuführen hat, sich vorhandene Berichte von solchen Sonografien anschauen und sich so über relevante Untersuchungstechniken und Befunde vorab informieren.
  9. Milstead, J.L.: Thesauri in a full-text world (1998) 0.07
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    Date
    22. 9.1997 19:16:05
    Source
    Visualizing subject access for 21st century information resources: Papers presented at the 1997 Clinic on Library Applications of Data Processing, 2-4 Mar 1997, Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign. Ed.: P.A. Cochrane et al
  10. Needham, R.M.; Sparck Jones, K.: Keywords and clumps (1985) 0.06
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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.
    Source
    Theory of subject analysis: a sourcebook. Ed.: L.M. Chan, et al
  11. 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
  12. Husevag, A.-S.R.: Named entities in indexing : a case study of TV subtitles and metadata records (2016) 0.04
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    Source
    Proceedings of the 15th European Networked Knowledge Organization Systems Workshop (NKOS 2016) co-located with the 20th International Conference on Theory and Practice of Digital Libraries 2016 (TPDL 2016), Hannover, Germany, September 9, 2016. Edi. by Philipp Mayr et al. [http://ceur-ws.org/Vol-1676/=urn:nbn:de:0074-1676-5]
  13. Strobel, S.; Marín-Arraiza, P.: Metadata for scientific audiovisual media : current practices and perspectives of the TIB / AV-portal (2015) 0.04
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    Source
    Metadata and semantics research: 9th Research Conference, MTSR 2015, Manchester, UK, September 9-11, 2015, Proceedings. Eds.: E. Garoufallou et al
  14. Ma, N.; Zheng, H.T.; Xiao, X.: ¬An ontology-based latent semantic indexing approach using long short-term memory networks (2017) 0.04
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    Source
    Web and Big Data: First International Joint Conference, APWeb-WAIM 2017, Beijing, China, July 7-9, 2017, Proceedings, Part I. Eds.: L. Chen et al
  15. Salton, G.: Automatic processing of foreign language documents (1985) 0.03
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    Source
    Theory of subject analysis: a sourcebook. Ed.: L.M. Chan, et al
  16. Chartron, G.; Dalbin, S.; Monteil, M.-G.; Verillon, M.: Indexation manuelle et indexation automatique : dépasser les oppositions (1989) 0.02
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  17. Fuhr, N.: Probabilistisches Indexing and Retrieval (1988) 0.02
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    Type
    r
  18. Kuhlen, R.: Morphologische Relationen durch Reduktionsalgorithmen (1974) 0.02
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  19. Salton, G.: Future prospects for text-based information retrieval (1990) 0.02
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    Source
    Pragmatische Aspekte beim Entwurf und Betrieb von Informationssystemen: Proc. des 1. Int. Symposiums für Informationswissenschaft, Universität Konstanz, 17.-19.10.1990. Hrsg.: J. Herget u. R. Kuhlen
  20. Reimer, U.: Verfahren der automatischen Indexierung : benötigtes Vorwissen und Ansätze zu seiner automatischen Akquisition, ein Überblick (1992) 0.02
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    Source
    Experimentelles und praktisches Information Retrieval: Festschrift für Gerhard Lustig. Hrsg. R. Kuhlen

Years

Languages

  • e 39
  • d 33
  • f 3
  • m 1
  • ru 1
  • More… Less…

Types

  • a 64
  • el 5
  • s 4
  • x 4
  • m 3
  • r 2
  • d 1
  • More… Less…

Classifications