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  • × theme_ss:"Automatisches Klassifizieren"
  1. Hotho, A.; Bloehdorn, S.: Data Mining 2004 : Text classification by boosting weak learners based on terms and concepts (2004) 0.11 
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    Content
    Vgl.: http://www.google.de/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CEAQFjAA&url=http%3A%2F%2Fciteseerx.ist.psu.edu%2Fviewdoc%2Fdownload%3Fdoi%3D10.1.1.91.4940%26rep%3Drep1%26type%3Dpdf&ei=dOXrUMeIDYHDtQahsIGACg&usg=AFQjCNHFWVh6gNPvnOrOS9R3rkrXCNVD-A&sig2=5I2F5evRfMnsttSgFF9g7Q&bvm=bv.1357316858,d.Yms.
    Date
    8. 1.2013 10:22:32
  2. Maghsoodi, N.; Homayounpour, M.M.: Improving Farsi multiclass text classification using a thesaurus and two-stage feature selection (2011) 0.03 
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    Abstract
    The progressive increase of information content has recently made it necessary to create a system for automatic classification of documents. In this article, a system is presented for the categorization of multiclass Farsi documents that requires fewer training examples and can help to compensate the shortcoming of the standard training dataset. The new idea proposed in the present article is based on extending the feature vector by adding some words extracted from a thesaurus and then filtering the new feature vector by applying secondary feature selection to discard inappropriate features. In fact, a phase of secondary feature selection is applied to choose more appropriate features among the features added from a thesaurus to enhance the effect of using a thesaurus on the efficiency of the classifier. To evaluate the proposed system, a corpus is gathered from the Farsi Wikipedia website and some articles in the Hamshahri newspaper, the Roshd periodical, and the Soroush magazine. In addition to studying the role of a thesaurus and applying secondary feature selection, the effect of a various number of categories, size of the training dataset, and average number of words in the test data also are examined. As the results indicate, classification efficiency improves by applying this approach, especially when available data is not sufficient for some text categories.
  3. Koch, T.; Ardö, A.: Automatic classification of full-text HTML-documents from one specific subject area : DESIRE II D3.6a, Working Paper 2 (2000) 0.02 
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    Content
    1 Introduction / 2 Method overview / 3 Ei thesaurus preprocessing / 4 Automatic classification process: 4.1 Matching -- 4.2 Weighting -- 4.3 Preparation for display / 5 Results of the classification process / 6 Evaluations / 7 Software / 8 Other applications / 9 Experiments with universal classification systems / References / Appendix A: Ei classification service: Software / Appendix B: Use of the classification software as subject filter in a WWW harvester.
  4. Koch, T.; Vizine-Goetz, D.: Automatic classification and content navigation support for Web services : DESIRE II cooperates with OCLC (1998) 0.02 
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    Abstract
    Emerging standards in knowledge representation and organization are preparing the way for distributed vocabulary support in Internet search services. NetLab researchers are exploring several innovative solutions for searching and browsing in the subject-based Internet gateway, Electronic Engineering Library, Sweden (EELS). The implementation of the EELS service is described, specifically, the generation of the robot-gathered database 'All' engineering and the automated application of the Ei thesaurus and classification scheme. NetLab and OCLC researchers are collaborating to investigate advanced solutions to automated classification in the DESIRE II context. A plan for furthering the development of distributed vocabulary support in Internet search services is offered.
  5. Subramanian, S.; Shafer, K.E.: Clustering (2001) 0.02 
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    Date
    5. 5.2003 14:17:22
  6. Golub, K.: Automated subject classification of textual Web pages, based on a controlled vocabulary : challenges and recommendations (2006) 0.01 
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    Abstract
    The primary objective of this study was to identify and address problems of applying a controlled vocabulary in automated subject classification of textual Web pages, in the area of engineering. Web pages have special characteristics such as structural information, but are at the same time rather heterogeneous. The classification approach used comprises string-to-string matching between words in a term list extracted from the Ei (Engineering Information) thesaurus and classification scheme, and words in the text to be classified. Based on a sample of 70 Web pages, a number of problems with the term list are identified. Reasons for those problems are discussed and improvements proposed. Methods for implementing the improvements are also specified, suggesting further research.
  7. Golub, K.; Hamon, T.; Ardö, A.: Automated classification of textual documents based on a controlled vocabulary in engineering (2007) 0.01 
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    Abstract
    Automated subject classification has been a challenging research issue for many years now, receiving particular attention in the past decade due to rapid increase of digital documents. The most frequent approach to automated classification is machine learning. It, however, requires training documents and performs well on new documents only if these are similar enough to the former. We explore a string-matching algorithm based on a controlled vocabulary, which does not require training documents - instead it reuses the intellectual work put into creating the controlled vocabulary. Terms from the Engineering Information thesaurus and classification scheme were matched against title and abstract of engineering papers from the Compendex database. Simple string-matching was enhanced by several methods such as term weighting schemes and cut-offs, exclusion of certain terms, and en- richment of the controlled vocabulary with automatically extracted terms. The best results are 76% recall when the controlled vocabulary is enriched with new terms, and 79% precision when certain terms are excluded. Precision of individual classes is up to 98%. These results are comparable to state-of-the-art machine-learning algorithms.
  8. Reiner, U.: Automatische DDC-Klassifizierung von bibliografischen Titeldatensätzen (2009) 0.01 
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    Date
    22. 8.2009 12:54:24
  9. HaCohen-Kerner, Y. et al.: Classification using various machine learning methods and combinations of key-phrases and visual features (2016) 0.01 
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    Date
    1. 2.2016 18:25:22
  10. Groß, T.; Faden, M.: Automatische Indexierung elektronischer Dokumente an der Deutschen Zentralbibliothek für Wirtschaftswissenschaften : Bericht über die Jahrestagung der Internationalen Buchwissenschaftlichen Gesellschaft (2010) 0.01 
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    Abstract
    Mit der Anfang 2010 begonnen Implementierung und Ergebnisevaluierung des automatischen Indexierungsverfahrens "Decisiv Categorization" der Firma Recommind soll das hier skizzierte Informationsstrukturierungsproblem in zwei Schritten gelöst werden. Kurz- bis mittelfristig soll die intellektuelle Indexierung durch ein semiautomatisches Verfahren6 unterstützt werden. Mittel- bis langfristig soll das maschinelle Verfahren, aufbauend auf einem entsprechenden Training, in die Lage versetzt werden, sowohl im Hause vorliegende Dokumente vollautomatisch zu indexieren als auch ZBW-fremde digitale Informationsressourcen zu verschlagworten bzw. zu klassifizieren, um sie in einem gemeinsamen Suchraum auffindbar machen zu können. Im Anschluss an diese Einleitung werden die ersten Ansätze maschineller Sacherschließung an der ZBW (2001-2004) und deren Ergebnisse und Problemlagen aufgezeigt. Danach werden die Rahmenbedingungen (Projektauftrag und -ziel) für eine Wiederaufnahme des Vorhabens im Jahre 2009 aufgezeigt, gefolgt von einer Darstellung der Funktionsweise der Recommind-Technologie und deren Einsatz im Rahmen der Sacherschließung von Online-Dokumenten mit einem Thesaurus. Schwerpunkt dieser Abhandlung bilden im Anschluss daran die Evaluierungsmöglichkeiten automatischer Indexierungsansätze sowie die aktuellen Ergebnisse und zentralen Erkenntnisse des Einsatzes im Kontext der ZBW. Das Fazit beschreibt die entsprechenden Schlussfolgerungen aus den erzielten Ergebnissen sowie den Ausblick auf das weitere Vorgehen.
    Object
    Standard-Thesaurus Wirtschaft
  11. Chung, Y.M.; Lee, J.Y.: ¬A corpus-based approach to comparative evaluation of statistical term association measures (2001) 0.01 
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    Abstract
    Statistical association measures have been widely applied in information retrieval research, usually employing a clustering of documents or terms on the basis of their relationships. Applications of the association measures for term clustering include automatic thesaurus construction and query expansion. This research evaluates the similarity of six association measures by comparing the relationship and behavior they demonstrate in various analyses of a test corpus. Analysis techniques include comparisons of highly ranked term pairs and term clusters, analyses of the correlation among the association measures using Pearson's correlation coefficient and MDS mapping, and an analysis of the impact of a term frequency on the association values by means of z-score. The major findings of the study are as follows: First, the most similar association measures are mutual information and Yule's coefficient of colligation Y, whereas cosine and Jaccard coefficients, as well as X**2 statistic and likelihood ratio, demonstrate quite similar behavior for terms with high frequency. Second, among all the measures, the X**2 statistic is the least affected by the frequency of terms. Third, although cosine and Jaccard coefficients tend to emphasize high frequency terms, mutual information and Yule's Y seem to overestimate rare terms
  12. Wartena, C.; Sommer, M.: Automatic classification of scientific records using the German Subject Heading Authority File (SWD) (2012) 0.01 
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    Abstract
    The following paper deals with an automatic text classification method which does not require training documents. For this method the German Subject Heading Authority File (SWD), provided by the linked data service of the German National Library is used. Recently the SWD was enriched with notations of the Dewey Decimal Classification (DDC). In consequence it became possible to utilize the subject headings as textual representations for the notations of the DDC. Basically, we we derive the classification of a text from the classification of the words in the text given by the thesaurus. The method was tested by classifying 3826 OAI-Records from 7 different repositories. Mean reciprocal rank and recall were chosen as evaluation measure. Direct comparison to a machine learning method has shown that this method is definitely competitive. Thus we can conclude that the enriched version of the SWD provides high quality information with a broad coverage for classification of German scientific articles.
  13. Bock, H.-H.: Datenanalyse zur Strukturierung und Ordnung von Information (1989) 0.01 
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    Pages
    S.1-22
  14. Dubin, D.: Dimensions and discriminability (1998) 0.01 
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    Date
    22. 9.1997 19:16:05
  15. Automatic classification research at OCLC (2002) 0.01 
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    Date
    5. 5.2003 9:22:09
  16. Jenkins, C.: Automatic classification of Web resources using Java and Dewey Decimal Classification (1998) 0.01 
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    Date
    1. 8.1996 22:08:06
  17. Yoon, Y.; Lee, C.; Lee, G.G.: ¬An effective procedure for constructing a hierarchical text classification system (2006) 0.01 
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    Date
    22. 7.2006 16:24:52
  18. Yi, K.: Automatic text classification using library classification schemes : trends, issues and challenges (2007) 0.01 
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    Date
    22. 9.2008 18:31:54
  19. Liu, R.-L.: Context recognition for hierarchical text classification (2009) 0.01 
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    Date
    22. 3.2009 19:11:54
  20. Pfeffer, M.: Automatische Vergabe von RVK-Notationen mittels fallbasiertem Schließen (2009) 0.01 
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    Date
    22. 8.2009 19:51:28

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