Search (150 results, page 8 of 8)

  • × language_ss:"e"
  • × theme_ss:"Automatisches Klassifizieren"
  1. Fagni, T.; Sebastiani, F.: Selecting negative examples for hierarchical text classification: An experimental comparison (2010) 0.00 
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    Source
    Journal of the American Society for Information Science and Technology. 61(2010) no.11, S.2256-2265
  2. 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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    Source
    Journal of the American Society for Information Science and Technology. 63(2012) no.5, S.889-903
  3. 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
  4. 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.
  5. 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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    Source
    Journal of the Association for Information Science and Technology. 67(2016) no.7, S.1639-1667
  6. 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
  7. 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
  8. 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
  9. Ahmed, M.; Mukhopadhyay, M.; Mukhopadhyay, P.: Automated knowledge organization : AI ML based subject indexing system for libraries (2023) 0.00 
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    DESIDOC journal of library and information technology. 43(2023) no.1, S.45-54
  10. 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.

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