Search (60 results, page 2 of 3)

  • × theme_ss:"Automatisches Indexieren"
  • × year_i:[2010 TO 2020}
  1. Lichtenstein, A.; Plank, M.; Neumann, J.: TIB's portal for audiovisual media : combining manual and automatic indexing (2014) 0.00 
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  2. Siebenkäs, A.; Markscheffel, B.: Conception of a workflow for the semi-automatic construction of a thesaurus for the German printing industry (2015) 0.00 
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  3. Keller, A.: Attitudes among German- and English-speaking librarians toward (automatic) subject indexing (2015) 0.00 
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  4. Daudaravicius, V.: ¬A framework for keyphrase extraction from scientific journals (2016) 0.00 
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  5. Gábor, K.; Zargayouna, H.; Tellier, I.; Buscaldi, D.; Charnois, T.: ¬A typology of semantic relations dedicated to scientific literature analysis (2016) 0.00 
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  6. Munkelt, J.: Erstellung einer DNB-Retrieval-Testkollektion (2018) 0.00 
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  7. Wolfe, EW.: a case study in automated metadata enhancement : Natural Language Processing in the humanities (2019) 0.00 
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  8. Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.00 
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  9. Chung, E.-K.; Miksa, S.; Hastings, S.K.: ¬A framework of automatic subject term assignment for text categorization : an indexing conception-based approach (2010) 0.00 
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    Abstract
    The purpose of this study is to examine whether the understandings of subject-indexing processes conducted by human indexers have a positive impact on the effectiveness of automatic subject term assignment through text categorization (TC). More specifically, human indexers' subject-indexing approaches, or conceptions, in conjunction with semantic sources were explored in the context of a typical scientific journal article dataset. Based on the premise that subject indexing approaches or conceptions with semantic sources are important for automatic subject term assignment through TC, this study proposed an indexing conception-based framework. For the purpose of this study, two research questions were explored: To what extent are semantic sources effective? To what extent are indexing conceptions effective? The experiments were conducted using a Support Vector Machine implementation in WEKA (I.H. Witten & E. Frank, [2000]). Using F-measure, the experiment results showed that cited works, source title, and title were as effective as the full text while a keyword was found more effective than the full text. In addition, the findings showed that an indexing conception-based framework was more effective than the full text. The content-oriented and the document-oriented indexing approaches especially were found more effective than the full text. Among three indexing conception-based approaches, the content-oriented approach and the document-oriented approach were more effective than the domain-oriented approach. In other words, in the context of a typical scientific journal article dataset, the objective contents and authors' intentions were more desirable for automatic subject term assignment via TC than the possible users' needs. The findings of this study support that incorporation of human indexers' indexing approaches or conception in conjunction with semantic sources has a positive impact on the effectiveness of automatic subject term assignment.
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  10. Mao, J.; Xu, W.; Yang, Y.; Wang, J.; Yuille, A.L.: Explain images with multimodal recurrent neural networks (2014) 0.00 
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  11. Benson, A.C.: Image descriptions and their relational expressions : a review of the literature and the issues (2015) 0.00 
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  12. Karpathy, A.; Fei-Fei, L.: Deep visual-semantic alignments for generating image descriptions (2015) 0.00 
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  13. Kiros, R.; Salakhutdinov, R.; Zemel, R.S.: Unifying visual-semantic embeddings with multimodal neural language models (2014) 0.00 
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  14. Banerjee, K.; Johnson, M.: Improving access to archival collections with automated entity extraction (2015) 0.00 
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  15. Fauzi, F.; Belkhatir, M.: Multifaceted conceptual image indexing on the world wide web (2013) 0.00 
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  16. 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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  17. Zhitomirsky-Geffet, M.; Prebor, G.; Bloch, O.: Improving proverb search and retrieval with a generic multidimensional ontology (2017) 0.00 
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  18. Gil-Leiva, I.: SISA-automatic indexing system for scientific articles : experiments with location heuristics rules versus TF-IDF rules (2017) 0.00 
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  19. Munkelt, J.; Schaer, P.; Lepsky, K.: Towards an IR test collection for the German National Library (2018) 0.00 
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  20. Golub, K.; Lykke, M.; Tudhope, D.: Enhancing social tagging with automated keywords from the Dewey Decimal Classification (2014) 0.00 
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