Search (159 results, page 8 of 8)

  • × theme_ss:"Automatisches Klassifizieren"
  1. Yao, H.; Etzkorn, L.H.; Virani, S.: Automated classification and retrieval of reusable software components (2008) 0.00 
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    Journal of the American Society for Information Science and Technology. 59(2008) no.4, S.613-627
  2. Pong, J.Y.-H.; Kwok, R.C.-W.; Lau, R.Y.-K.; Hao, J.-X.; Wong, P.C.-C.: ¬A comparative study of two automatic document classification methods in a library setting (2008) 0.00 
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    Source
    Journal of information science. 34(2008) no.2, S.213-230
  3. Mengle, S.S.R.; Goharian, N.: Ambiguity measure feature-selection algorithm (2009) 0.00 
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    Journal of the American Society for Information Science and Technology. 60(2009) no.5, S.1037-1050
  4. Wang, J.: ¬An extensive study on automated Dewey Decimal Classification (2009) 0.00 
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    Journal of the American Society for Information Science and Technology. 60(2009) no.11, S.2269-2286
  5. Kishida, K.: High-speed rough clustering for very large document collections (2010) 0.00 
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    Journal of the American Society for Information Science and Technology. 61(2010) no.6, S.1092-1104
  6. HaCohen-Kerner, Y.; Beck, H.; Yehudai, E.; Rosenstein, M.; Mughaz, D.: Cuisine : classification using stylistic feature sets and/or name-based feature sets (2010) 0.00 
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    Journal of the American Society for Information Science and Technology. 61(2010) no.8, S.1644-1657
  7. Fagni, T.; Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison (2010) 0.00 
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    Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2256-2265
  8. Qu, B.; Cong, G.; Li, C.; Sun, A.; Chen, H.: ¬An evaluation of classification models for question topic categorization (2012) 0.00 
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    Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.889-903
  9. Alberts, I.; Forest, D.: Email pragmatics and automatic classification : a study in the organizational context (2012) 0.00 
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    Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.904-922
  10. Wartena, C.; Sommer, M.: Automatic classification of scientific records using the German Subject Heading Authority File (SWD) (2012) 0.00 
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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.
  11. Borodin, Y.; Polishchuk, V.; Mahmud, J.; Ramakrishnan, I.V.; Stent, A.: Live and learn from mistakes : a lightweight system for document classification (2013) 0.00 
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    Information processing and management. 49(2013) no.1, S.83-98
  12. Salles, T.; Rocha, L.; Gonçalves, M.A.; Almeida, J.M.; Mourão, F.; Meira Jr., W.; Viegas, F.: ¬A quantitative analysis of the temporal effects on automatic text classification (2016) 0.00 
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    Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1639-1667
  13. Suominen, A.; Toivanen, H.: Map of science with topic modeling : comparison of unsupervised learning and human-assigned subject classification (2016) 0.00 
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    Journal of the Association for Information Science and Technology. 67(2016) no.10, S.2464-2476
  14. Wang, H.; Hong, M.: Supervised Hebb rule based feature selection for text classification (2019) 0.00 
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    Information processing and management. 56(2019) no.1, S.167-191
  15. Ru, C.; Tang, J.; Li, S.; Xie, S.; Wang, T.: Using semantic similarity to reduce wrong labels in distant supervision for relation extraction (2018) 0.00 
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    Information processing and management. 54(2018) no.4, S.593-608
  16. Pech, G.; Delgado, C.; Sorella, S.P.: Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures : an application in Physics (2022) 0.00 
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    Journal of the Association for Information Science and Technology. 73(2022) no.11, S.1513-1528
  17. Piros, A.: Automatic interpretation of complex UDC numbers : towards support for library systems (2015) 0.00 
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    Abstract
    Analytico-synthetic and faceted classifications, such as Universal Decimal Classification (UDC) express content of documents with complex, pre-combined classification codes. Without classification authority control that would help manage and access structured notations, the use of UDC codes in searching and browsing is limited. Existing UDC parsing solutions are usually created for a particular database system or a specific task and are not widely applicable. The approach described in this paper provides a solution by which the analysis and interpretation of UDC notations would be stored into an intermediate format (in this case, in XML) by automatic means without any data or information loss. Due to its richness, the output file can be converted into different formats, such as standard mark-up and data exchange formats or simple lists of the recommended entry points of a UDC number. The program can also be used to create authority records containing complex UDC numbers which can be comprehensively analysed in order to be retrieved effectively. The Java program, as well as the corresponding schema definition it employs, is under continuous development. The current version of the interpreter software is now available online for testing purposes at the following web site: http://interpreter-eto.rhcloud.com. The future plan is to implement conversion methods for standard formats and to create standard online interfaces in order to make it possible to use the features of software as a service. This would result in the algorithm being able to be employed both in existing and future library systems to analyse UDC numbers without any significant programming effort.
  18. Kragelj, M.; Borstnar, M.K.: Automatic classification of older electronic texts into the Universal Decimal Classification-UDC (2021) 0.00 
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    Abstract
    Purpose The purpose of this study is to develop a model for automated classification of old digitised texts to the Universal Decimal Classification (UDC), using machine-learning methods. Design/methodology/approach The general research approach is inherent to design science research, in which the problem of UDC assignment of the old, digitised texts is addressed by developing a machine-learning classification model. A corpus of 70,000 scholarly texts, fully bibliographically processed by librarians, was used to train and test the model, which was used for classification of old texts on a corpus of 200,000 items. Human experts evaluated the performance of the model. Findings Results suggest that machine-learning models can correctly assign the UDC at some level for almost any scholarly text. Furthermore, the model can be recommended for the UDC assignment of older texts. Ten librarians corroborated this on 150 randomly selected texts. Research limitations/implications The main limitations of this study were unavailability of labelled older texts and the limited availability of librarians. Practical implications The classification model can provide a recommendation to the librarians during their classification work; furthermore, it can be implemented as an add-on to full-text search in the library databases. Social implications The proposed methodology supports librarians by recommending UDC classifiers, thus saving time in their daily work. By automatically classifying older texts, digital libraries can provide a better user experience by enabling structured searches. These contribute to making knowledge more widely available and useable. Originality/value These findings contribute to the field of automated classification of bibliographical information with the usage of full texts, especially in cases in which the texts are old, unstructured and in which archaic language and vocabulary are used.
  19. Oberhauser, O.: Automatisches Klassifizieren : Entwicklungsstand - Methodik - Anwendungsbereiche (2005) 0.00 
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    Footnote
    Die am Anfang des Werkes gestellte Frage, ob »die Techniken des automatischen Klassifizierens heute bereits so weit [sind], dass damit grosse Mengen elektronischer Dokumente [-] zufrieden stellend erschlossen werden können? « (S. 13), beantwortet der Verfasser mit einem eindeutigen »nein«, was Salton und McGills Aussage von 1983, »daß einfache automatische Indexierungsverfahren schnell und kostengünstig arbeiten, und daß sie Recall- und Precisionwerte erreichen, die mindestens genauso gut sind wie bei der manuellen Indexierung mit kontrolliertem Vokabular « (Gerard Salton und Michael J. McGill: Information Retrieval. Hamburg u.a. 1987, S. 64 f.) kräftig relativiert. Über die Gründe, warum drei der großen Projekte nicht weiter verfolgt werden, will Oberhauser nicht spekulieren, nennt aber mangelnden Erfolg, Verlagerung der Arbeit in den beteiligten Institutionen sowie Finanzierungsprobleme als mögliche Ursachen. Das größte Entwicklungspotenzial beim automatischen Erschließen großer Dokumentenmengen sieht der Verfasser heute in den Bereichen der Patentund Mediendokumentation. Hier solle man im bibliothekarischen Bereich die Entwicklung genau verfolgen, da diese »sicherlich mittelfristig auf eine qualitativ zufrieden stellende Vollautomatisierung« abziele (S. 146). Oberhausers Darstellung ist ein rundum gelungenes Werk, das zum Handapparat eines jeden, der sich für automatische Erschließung interessiert, gehört."

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