Atıf İçin Kopyala
AKBULUT M., Tonta Y.
TURKISH LIBRARIANSHIP, cilt.36, sa.2, ss.169-203, 2022 (ESCI)
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Yayın Türü:
Makale / Tam Makale
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Cilt numarası:
36
Sayı:
2
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Basım Tarihi:
2022
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Doi Numarası:
10.24146/tk.1062751
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Dergi Adı:
TURKISH LIBRARIANSHIP
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Derginin Tarandığı İndeksler:
Emerging Sources Citation Index (ESCI), Library and Information Science Abstracts, Library, Information Science & Technology Abstracts (LISTA), Directory of Open Access Journals, TR DİZİN (ULAKBİM)
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Sayfa Sayıları:
ss.169-203
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Anahtar Kelimeler:
Relevance rankings, probabilistic topic modeling, the Latent Dirichlet Allocation (LDA) algorithm, pennant retrieval, Maximal Marginal Relevance (MMR), INTERDISCIPLINARY RESEARCH, COMBINING BIBLIOMETRICS, INFORMATION-RETRIEVAL, COCITATION, DISCOVERY, EXAMPLES, MODEL, TERM
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Hacettepe Üniversitesi Adresli:
Evet
Özet
Purpose: Relevance ranking algorithms rank retrieved documents based on the degrees of topical similarity (relevance) between search queries and documents. This paper aims to introduce a new relevance ranking method combining a probabilistic topic modeling algorithm with citation data.