يعرض 1 - 20 نتائج من 42 نتيجة بحث عن '"topic labeling"', وقت الاستعلام: 0.48s تنقيح النتائج
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    Academic Journal
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    Academic Journal

    المصدر: Information; Volume 13; Issue 10; Pages: 444

    مصطلحات موضوعية: zero-shot, topic labeling, hazard classification

    وصف الملف: application/pdf

    Relation: Artificial Intelligence; https://dx.doi.org/10.3390/info13100444

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    Academic Journal

    المؤلفون: Tetyana Loskutova

    المصدر: Journal of Systemics, Cybernetics and Informatics, Vol 17, Iss 4, Pp 1-5 (2019)

    وصف الملف: electronic resource

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    Academic Journal
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    Academic Journal
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    Academic Journal

    المساهمون: HKÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümü, orcid:0000-0002-2130-5503

    مصطلحات موضوعية: Content analyses, IFRC, LDA, Machine learning, Topic labeling, Twitter

    وصف الملف: application/pdf

    Relation: Natural Hazards; Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı; Dereli, T., Eliguzel, N., & Cetinkaya, C. (January 01, 2021). Content analyses of the international federation of red cross and red crescent societies (ifrc) based on machine learning techniques through twitter. Natural Hazards.; https://doi.org/10.1007/s11069-021-04527-w; https://hdl.handle.net/20.500.11782/2307

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    Dissertation/ Thesis
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    Academic Journal
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    المساهمون: Koopman, R (OCLC Research)

    المصدر: Data was extracted from a corpus of bibliographic data indexed by the Web of Science of Thompson Reuters (now: Clarivate Analytics) of 111,616 papers in 59 journals in Astrophysics and Astronomy published between 2003-2011. Thanks to Kevin Boyack (SciTech Strategies), all items in the corpus were indexed with thesaurus terms of the Unified Astronomy Thesaurus (UAT, http://astrothesaurus.org) pro bono by Access Innovations, using their MAI (Machine Aided Indexer) software package and the UAT rule base that they maintain.

    Time: 2003-2011

    وصف الملف: txt; csv; xslx; gephi; gexf

    Relation: title=Velden, T., Boyack, K.W., Gläser, J., Koopman, R., Scharnhorst, A., and Wang, S. (2017), 'Comparison of Topic Extraction Approaches And Their Results'. In: Gläser, J., Scharnhorst, A. & Glänzel, W. (eds), 'Same data – different results? Towards a comparative approach to the identification of thematic structures in science, Special Issue of Scientometrics'. DOI:10.1007/s11192-017-2306-1; URI=https://doi.org/10.1007/s11192-017-2306-1; http://nbn-resolving.org/urn:nbn:nl:ui:13-l4bb-i3; https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:68020

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    Conference

    المساهمون: Stella, F, Ciucci, D, Calegari, S, Magatti, D

    Relation: info:eu-repo/semantics/altIdentifier/isbn/978-0-7695-3872-3; info:eu-repo/semantics/altIdentifier/wos/WOS:000288405800209; ispartofbook:An Efficient Combinatorial Approach for Solving the DNA Motif Finding Problem; International Conference on Intelligent Systems Design and Applications; http://hdl.handle.net/10281/8357; info:eu-repo/semantics/altIdentifier/scopus/2-s2.0-77949534940

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    Academic Journal