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• W4245616207 abstract "Abstract In this paper, we present a theoretical analysis and extensive experiments on the automated assignment of Dewey Decimal Classification (DDC) classes to bibliographic data with a supervised machine‐learning approach. Library classification systems, such as the DDC, impose great obstacles on state‐of‐art text categorization (TC) technologies, including deep hierarchy, data sparseness, and skewed distribution. We first analyze statistically the document and category distributions over the DDC, and discuss the obstacles imposed by bibliographic corpora and library classification schemes on TC technology. To overcome these obstacles, we propose an innovative algorithm to reshape the DDC structure into a balanced virtual tree by balancing the category distribution and flattening the hierarchy. To improve the classification effectiveness to a level acceptable to real‐world applications, we propose an interactive classification model that is able to predict a class of any depth within a limited number of user interactions. The experiments are conducted on a large bibliographic collection created by the Library of Congress within the science and technology domains over 10 years. With no more than three interactions, a classification accuracy of nearly 90% is achieved, thus providing a practical solution to the automatic bibliographic classification problem." @default.
• W4245616207 created "2022-05-12" @default.
• W4245616207 creator A5042241049 @default.
• W4245616207 date "2009-07-06" @default.
• W4245616207 modified "2023-10-16" @default.
• W4245616207 title "An extensive study on automated Dewey Decimal Classification" @default.
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• W4245616207 doi "https://doi.org/10.1002/asi.21147" @default.
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