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AKBULUT M., Tonta Y.
TURKISH LIBRARIANSHIP, vol.36, no.2, pp.169-203, 2022 (ESCI)
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Publication Type:
Article / Article
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Volume:
36
Issue:
2
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Publication Date:
2022
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Doi Number:
10.24146/tk.1062751
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Journal Name:
TURKISH LIBRARIANSHIP
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Journal Indexes:
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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Page Numbers:
pp.169-203
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Keywords:
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 University Affiliated:
Yes
Abstract
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.