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  • × theme_ss:"Automatisches Indexieren"
  1. Mansour, N.; Haraty, R.A.; Daher, W.; Houri, M.: ¬An auto-indexing method for Arabic text (2008) 0.00 
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    Information processing and management. 44(2008) no.4, S.1538-1545
  2. Kempf, A.O.: Automatische Indexierung in der sozialwissenschaftlichen Fachinformation : eine Evaluationsstudie zur maschinellen Erschließung für die Datenbank SOLIS (2012) 0.00 
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    Abstract
    Automatische Indexierungsverfahren werden mit Zunahme der digitalen Verfügbarkeit von Metadaten und Volltexten mehr und mehr als eine mögliche Antwort auf das Management unstrukturierter Daten diskutiert. In der sozialwissenschaftlichen Fachinformation existiert in diesem Zusammenhang seit einiger Zeit der Vorschlag eines sogenannten Schalenmodells (vgl. Krause, 1996) mit unterschiedlichen Qualitätsstufen bei der inhaltlichen Erschließung. Vor diesem Hintergrund beschreibt die Arbeit zunächst Methoden und Verfahren der inhaltlichen und automatischen Indexierung, bevor vier Testläufe eines automatischen Indexierungssystems (MindServer) zur automatischen Erschließung von Datensätzen der bibliographischen Literaturdatenbank SOLIS mit Deskriptoren des Thesaurus Sozialwissenschaften sowie der Klassifikation Sozialwissenschaften beschrieben und analysiert werden. Es erfolgt eine ausführliche Fehleranalyse mit Beispielen sowie eine abschließende Diskussion, inwieweit die automatische Erschließung in dieser Form für die Randbereiche der Datenbank SOLIS für die Zukunft einen gangbaren Weg darstellt.
  3. Fauzi, F.; Belkhatir, M.: Multifaceted conceptual image indexing on the world wide web (2013) 0.00 
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    Source
    Information processing and management. 49(2013) no.2, S.420-440
  4. Snajder, J.; Dalbelo Basic, B.D.; Tadic, M.: Automatic acquisition of inflectional lexica for morphological normalisation (2008) 0.00 
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    Source
    Information processing and management. 44(2008) no.5, S.1720-1731
  5. Flores, F.N.; Moreira, V.P.: Assessing the impact of stemming accuracy on information retrieval : a multilingual perspective (2016) 0.00 
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    Information processing and management. 52(2016) no.5, S.840-854
  6. Plaunt, C.; Norgard, B.A.: ¬An association-based method for automatic indexing with a controlled vocabulary (1998) 0.00 
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    Date
    11. 9.2000 19:53:22
  7. Milstead, J.L.: Thesauri in a full-text world (1998) 0.00 
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    Date
    22. 9.1997 19:16:05
  8. Junger, U.; Schwens, U.: ¬Die inhaltliche Erschließung des schriftlichen kulturellen Erbes auf dem Weg in die Zukunft : Automatische Vergabe von Schlagwörtern in der Deutschen Nationalbibliothek (2017) 0.00 
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    Date
    19. 8.2017 9:24:22
  9. Li, W.; Wong, K.-F.; Yuan, C.: Toward automatic Chinese temporal information extraction (2001) 0.00 
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    Abstract
    Over the past few years, temporal information processing and temporal database management have increasingly become hot topics. Nevertheless, only a few researchers have investigated these areas in the Chinese language. This lays down the objective of our research: to exploit Chinese language processing techniques for temporal information extraction and concept reasoning. In this article, we first study the mechanism for expressing time in Chinese. On the basis of the study, we then design a general frame structure for maintaining the extracted temporal concepts and propose a system for extracting time-dependent information from Hong Kong financial news. In the system, temporal knowledge is represented by different types of temporal concepts (TTC) and different temporal relations, including absolute and relative relations, which are used to correlate between action times and reference times. In analyzing a sentence, the algorithm first determines the situation related to the verb. This in turn will identify the type of temporal concept associated with the verb. After that, the relevant temporal information is extracted and the temporal relations are derived. These relations link relevant concept frames together in chronological order, which in turn provide the knowledge to fulfill users' queries, e.g., for question-answering (i.e., Q&A) applications
  10. Ahlgren, P.; Kekäläinen, J.: Indexing strategies for Swedish full text retrieval under different user scenarios (2007) 0.00 
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    Source
    Information processing and management. 43(2007) no.1, S.81-102
  11. Tsai, C.-F.; McGarry, K.; Tait, J.: Qualitative evaluation of automatic assignment of keywords to images (2006) 0.00 
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    Information processing and management. 42(2006) no.1, S.136-154
  12. Nohr, H.: Theorie des Information Retrieval II : Automatische Indexierung (2004) 0.00 
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    Abstract
    Ein großer Teil der Informationen - Schätzungen zufolge bis zu 80% - liegt in Organisationen in unstrukturierten Dokumenten vor. In der Vergangenheit wurden Lösungen für das Management strukturierter Informationen entwickelt, die es nun auch zu erreichen gilt für unstrukturierte Informationen. Neben Verfahren des Data Mining für die Datenanalyse treten Versuche, Text Mining (Lit. 06) auf die Textanalyse anzuwenden. Um gezielt Dokumente im Repository suchen zu können, ist eine effektive Inhaltserkennung und -kennzeichnung erforderlich, d.h. eine Zuordnung der Dokumente zu Themengebieten bzw die Speicherung geeigneter Indexterme als Metadaten. Zu diesem Zweck müssen die Dokumenteninhalte repräsentiert, d.h. indexiert oder klassifiziert, werden. Dokumentanalyse dient auch der Steuerung des Informations- und Dokumentenflusses. Ziel ist die Einleitung eines "Workflow nach Posteingang". Eine Dokumentanalyse kann anhand erkannter Merkmale Eingangspost automatisch an den Sachbearbeiter oder die zuständige Organisationseinheit (Rechnungen in die Buchhaltung, Aufträge in den Vertrieb) im Unternehmen leiten. Dokumentanalysen werden auch benötigt, wenn Mitarbeiter über einen persönlichen Informationsfilter relevante Dokumente automatisch zugestellt bekommen sollen. Aufgrund der Systemintegration werden Indexierungslösungen in den Funktionsumfang von DMS- bzw. Workflow-Produkten integriert. Eine Architektur solcher Systeme zeigt Abb. 1. Die Architektur zeigt die Indexierungs- bzw. Klassifizierungsfunktion im Zentrum der Anwendung. Dabei erfüllt sie Aufgaben für die Repräsentation von Dokumenten (Metadaten) und das spätere Retrieval.
  13. Witschel, H.F.: Terminology extraction and automatic indexing : comparison and qualitative evaluation of methods (2005) 0.00 
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    Abstract
    Many terminology engineering processes involve the task of automatic terminology extraction: before the terminology of a given domain can be modelled, organised or standardised, important concepts (or terms) of this domain have to be identified and fed into terminological databases. These serve in further steps as a starting point for compiling dictionaries, thesauri or maybe even terminological ontologies for the domain. For the extraction of the initial concepts, extraction methods are needed that operate on specialised language texts. On the other hand, many machine learning or information retrieval applications require automatic indexing techniques. In Machine Learning applications concerned with the automatic clustering or classification of texts, often feature vectors are needed that describe the contents of a given text briefly but meaningfully. These feature vectors typically consist of a fairly small set of index terms together with weights indicating their importance. Short but meaningful descriptions of document contents as provided by good index terms are also useful to humans: some knowledge management applications (e.g. topic maps) use them as a set of basic concepts (topics). The author believes that the tasks of terminology extraction and automatic indexing have much in common and can thus benefit from the same set of basic algorithms. It is the goal of this paper to outline some methods that may be used in both contexts, but also to find the discriminating factors between the two tasks that call for the variation of parameters or application of different techniques. The discussion of these methods will be based on statistical, syntactical and especially morphological properties of (index) terms. The paper is concluded by the presentation of some qualitative and quantitative results comparing statistical and morphological methods.
  14. Li, X.; Zhang, A.; Li, C.; Ouyang, J.; Cai, Y.: Exploring coherent topics by topic modeling with term weighting (2018) 0.00 
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    Information processing and management. 54(2018) no.6, S.1345-1358
  15. Ahmed, M.: Automatic indexing for agriculture : designing a framework by deploying Agrovoc, Agris and Annif (2023) 0.00 
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    ¬SRELS Journal of Information Management. 60(2023) no.2, S.85-95
  16. 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) 0.00 
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    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
  17. Mesquita, L.A.P.; Souza, R.R.; Baracho Porto, R.M.A.: Noun phrases in automatic indexing: : a structural analysis of the distribution of relevant terms in doctoral theses (2014) 0.00 
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    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
  18. Search Engines and Beyond : Developing efficient knowledge management systems, April 19-20 1999, Boston, Mass (1999) 0.00 
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  19. SIGIR'92 : Proceedings of the 15th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1992) 0.00 
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    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
  20. Kaiser, A.: Computer-unterstütztes Indexieren in Intelligenten Information Retrieval Systemen : Ein Relevanz-Feedback orientierter Ansatz zur Informationserschließung in unformatierten Datenbanken (1993) 0.00 
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    Abstract
    Information ist in unserer Zeit zu einem sehr wichtigen Gut geworden. Sie ist Grundlage jeglicher seriösen Entscheidungsfindung. Die Informationsflut ist in den letzten Jahren stark angestiegen und auch in absehbarer Zeit wird die Menge der Informationen weiter anwachsen. Daher wird es immer wichtiger, sich ''Information über Information'' zu organisieren. Es ist nicht möglich, über alle Bereiche, mit denen man konfrontiert wird, im letzten Detail informiert zu sein. Notwendig und wichtig ist es aber zu wissen, wo man sich informieren kann. Relevante Informationen müssen möglichst rasch gefunden werden können. Im praktischen, EDV-unterstützten Einsatz findet man zu diesem Zweck Informationssysteme verschiedenster Art. Das Spektrum reicht dabei von Management-Informationssystemen, über Expertensysteme bis zu Datenbanksystemen und Information Retrieval Systemen (IR-Systemen). Obwohl die einzelnen Typen dieser informationsverarbeitenden Systeme für unterschiedliche Anwendergruppen und unterschiedliche Aufgabenarten konzipiert sind, ergeben sich beim Entwurf der Systeme doch sehr ähnlich gelagerte Problemkreise und Fragestellungen. * Die Darstellung und die Organisation von bestehendem Wissen und bekannten Fakten im Informationssystem (Informationserschließung). * Das (Wieder)finden relevanter Informationen aus dem Informationssystem und das Führen des Benutzers durch das Informationssystem. Ein Information Retrieval System beinhaltet unstrukturierte bibliographische oder textuelle Dokumente und unterscheidet sich dadurch wesentlich von Datenbanksystemen, die für gewöhnlich strukturierte Daten enthalten.

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