Search (97 results, page 2 of 5)

  • × type_ss:"el"
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  1. Hider, P.: ¬The search value added by professional indexing to a bibliographic database (2017) 0.01 
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    Abstract
    Gross et al. (2015) have demonstrated that about a quarter of hits would typically be lost to keyword searchers if contemporary academic library catalogs dropped their controlled subject headings. This paper reports on an analysis of the loss levels that would result if a bibliographic database, namely the Australian Education Index (AEI), were missing the subject descriptors and identifiers assigned by its professional indexers, employing the methodology developed by Gross and Taylor (2005), and later by Gross et al. (2015). The results indicate that AEI users would lose a similar proportion of hits per query to that experienced by library catalog users: on average, 27% of the resources found by a sample of keyword queries on the AEI database would not have been found without the subject indexing, based on the Australian Thesaurus of Education Descriptors (ATED). The paper also discusses the methodological limitations of these studies, pointing out that real-life users might still find some of the resources missed by a particular query through follow-up searches, while additional resources might also be found through iterative searching on the subject vocabulary. The paper goes on to describe a new research design, based on a before - and - after experiment, which addresses some of these limitations. It is argued that this alternative design will provide a more realistic picture of the value that professionally assigned subject indexing and controlled subject vocabularies can add to literature searching of a more scholarly and thorough kind.
  2. Sojka, P.; Lee, M.; Rehurek, R.; Hatlapatka, R.; Kucbel, M.; Bouche, T.; Goutorbe, C.; Anghelache, R.; Wojciechowski, K.: Toolset for entity and semantic associations : Final Release (2013) 0.01 
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    Abstract
    In this document we describe the final release of the toolset for entity and semantic associations, integrating two versions (language dependent and language independent) of Unsupervised Document Similarity implemented by MU (using gensim tool) and Citation Indexing, Resolution and Matching (UJF/CMD). We give a brief description of tools, the rationale behind decisions made, and provide elementary evaluation. Tools are integrated in the main project result, EuDML website, and they deliver the needed functionality for exploratory searching and browsing the collected documents. EuDML users and content providers thus benefit from millions of algorithmically generated similarity and citation links, developed using state of the art machine learning and matching methods.
    Object
    Latent Semantic Indexing
  3. Líska, M.; Sojka, P.: MIaS 1.5 (2014) 0.01 
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    Abstract
    A math-aware, full-text indexing based search engine that enables users to search for mathematical formulae inside documents. Search engine is unique because it is able to index and search structural information like representation of mathematical formulae. There is no other software or IR system that is able to store three billions of formulae in its index and search it with response time below a second. MIaS processes documents containing mathematical notation in MathML format. The system is built as an extension to any full-text indexing engine and has been verifiend on state-of-the-art Lucene core. It is scalable - it was verified to index almost whole arxiv.org (440,000 papers) having more than 160,000,000 formulae. Software is being used in EuDML (eudml.org) and other digital libraries. For more details see papers in peer reviewed conferences: [1] Sojka, Petr; Líska, Martin. In Matthew R. B. Hardy, Frank Wm. Tompa. Proceedings of the 2011 ACM Symposium on Document Engineering. Mountain View, CA, USA : ACM, 2011. pp.57--60. [2] Sojka, Petr; Líska, Martin. In J.H.Davenport, W.M. Farmer, J.Urban, F. Rabe. Intelligent Computer Mathematics LNCS 6824. Springer, 2011, pp.228--243.
  4. Drewer, P.; Massion, F; Pulitano, D: Was haben Wissensmodellierung, Wissensstrukturierung, künstliche Intelligenz und Terminologie miteinander zu tun? (2017) 0.01 
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    Date
    13.12.2017 14:17:22
  5. Schönherr, M.: Bestechend brillant : die Schönheit der Algorithmen (2016) 0.01 
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    Date
    10. 2.2016 17:22:23
  6. Häring, N.; Hensinger, P.: "Digitale Bildung" : Der abschüssige Weg zur Konditionierungsanstalt (2019) 0.01 
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    Date
    22. 2.2019 11:45:19
  7. Sojka, P.; Liska, M.: ¬The art of mathematics retrieval (2011) 0.01 
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    Content
    Vgl.: DocEng2011, September 19-22, 2011, Mountain View, California, USA Copyright 2011 ACM 978-1-4503-0863-2/11/09
    Date
    22. 2.2017 13:00:42
  8. Mitchell, J.S.; Zeng, M.L.; Zumer, M.: Modeling classification systems in multicultural and multilingual contexts (2012) 0.01 
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    Abstract
    This paper reports on the second part of an initiative of the authors on researching classification systems with the conceptual model defined by the Functional Requirements for Subject Authority Data (FRSAD) final report. In an earlier study, the authors explored whether the FRSAD conceptual model could be extended beyond subject authority data to model classification data. The focus of the current study is to determine if classification data modeled using FRSAD can be used to solve real-world discovery problems in multicultural and multilingual contexts. The paper discusses the relationships between entities (same type or different types) in the context of classification systems that involve multiple translations and /or multicultural implementations. Results of two case studies are presented in detail: (a) two instances of the DDC (DDC 22 in English, and the Swedish-English mixed translation of DDC 22), and (b) Chinese Library Classification. The use cases of conceptual models in practice are also discussed.
  9. Mai, F.; Galke, L.; Scherp, A.: Using deep learning for title-based semantic subject indexing to reach competitive performance to full-text (2018) 0.01 
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    Abstract
    For (semi-)automated subject indexing systems in digital libraries, it is often more practical to use metadata such as the title of a publication instead of the full-text or the abstract. Therefore, it is desirable to have good text mining and text classification algorithms that operate well already on the title of a publication. So far, the classification performance on titles is not competitive with the performance on the full-texts if the same number of training samples is used for training. However, it is much easier to obtain title data in large quantities and to use it for training than full-text data. In this paper, we investigate the question how models obtained from training on increasing amounts of title training data compare to models from training on a constant number of full-texts. We evaluate this question on a large-scale dataset from the medical domain (PubMed) and from economics (EconBiz). In these datasets, the titles and annotations of millions of publications are available, and they outnumber the available full-texts by a factor of 20 and 15, respectively. To exploit these large amounts of data to their full potential, we develop three strong deep learning classifiers and evaluate their performance on the two datasets. The results are promising. On the EconBiz dataset, all three classifiers outperform their full-text counterparts by a large margin. The best title-based classifier outperforms the best full-text method by 9.9%. On the PubMed dataset, the best title-based method almost reaches the performance of the best full-text classifier, with a difference of only 2.9%.
  10. Wicaksana, I.W.S.; Wahyudi, B.: Comparison Latent Semantic and WordNet approach for semantic similarity calculation (2011) 0.01 
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    Object
    Latent semantic indexing
  11. Klein, A.: Von der Schneeflocke zur Lawine : Möglichkeiten der Nutzung freier Zitationsdaten in Bibliotheken (2017) 0.01 
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    Theme
    Citation indexing
  12. Hartmann, S.; Haffner, A.: Linked-RDA-Data in der Praxis (2010) 0.01 
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    Date
    13. 2.2011 20:22:23
  13. Eckert, K.: SKOS: eine Sprache für die Übertragung von Thesauri ins Semantic Web (2011) 0.01 
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    Date
    15. 3.2011 19:21:22
  14. Scheven, E.: Geokoordinaten in Bibliotheksdaten : Grundlage für innovative Nachnutzung (2015) 0.01 
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    Date
    16.11.2015 18:22:47
  15. Hartmann, F.: Paul Otlets Hypermedium : Dokumentation als Gegenidee zur Bibliothek (2015) 0.01 
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    Date
    22. 8.2016 15:58:46
  16. Lezius, W.: Morphy - Morphologie und Tagging für das Deutsche (2013) 0.01 
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    Date
    22. 3.2015 9:30:24
  17. Schleim, S.: Warum die Wissenschaft nicht frei ist (2017) 0.01 
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    Date
    9.10.2017 15:48:22
  18. Rötzer, F.: Chinesischer Roboter besteht weltweit erstmals Zulassungsprüfung für Mediziner (2017) 0.01 
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    Issue
    [22. November 2017].
  19. Landwehr, A.: China schafft digitales Punktesystem für den "besseren" Menschen (2018) 0.01 
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    Date
    22. 6.2018 14:29:46
  20. Rötzer, F.: Psychologen für die Künstliche Intelligenz (2018) 0.01 
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    Date
    22. 1.2018 11:08:27

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