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Pejtersen, A.M.: Design of a classification scheme for fiction based on an analysis of actual user-librarian communication, and use of the scheme for control of librarians' search strategies (1980) 0.04

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Date: 5. 8.2006 13:22:44

Beghtol, C.: Toward a theory of fiction analysis for information storage and retrieval (1992) 0.04

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Date: 5. 8.2006 13:22:08
Source: Classification research for knowledge representation and organization. Proc. 5th Int. Study Conf. on Classification Research, Toronto, Canada, 24.-28.6.1991. Ed. by N.J. Williamson u. M. Hudon

Pejtersen, A.M.: ¬A new approach to the classification of fiction (1982) 0.02

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Source: Universal classification I: subject analysis and ordering systems. Proc. of the 4th Int. Study Conf. on Classification Research, Augsburg, 28.6.-2.7.1982. Ed. I. Dahlberg

Pejtersen, A.M.: Fiction and library classification (1978) 0.02

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Beghtol, C.: ¬The classification of fiction : the development of a system based on theoretical principles (1994) 0.01

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Abstract: The work is an adaptation of the author's dissertation and has the following chapters: (1) background and introduction; (2) a problem in classification theory; (3) previous fiction analysis theories and systems and 'The left hand of darkness'; (4) fiction warrant and critical warrant; (5) experimental fiction analysis system (EFAS); (6) application and evaluation of EFAS. Appendix 1 gives references to fiction analysis systems and appendix 2 lists EFAS coding sheets

Short, M.: Text mining and subject analysis for fiction; or, using machine learning and information extraction to assign subject headings to dime novels (2019) 0.01
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Abstract

This article describes multiple experiments in text mining at Northern Illinois University that were undertaken to improve the efficiency and accuracy of cataloging. It focuses narrowly on subject analysis of dime novels, a format of inexpensive fiction that was popular in the United States between 1860 and 1915. NIU holds more than 55,000 dime novels in its collections, which it is in the process of comprehensively digitizing. Classification, keyword extraction, named-entity recognition, clustering, and topic modeling are discussed as means of assigning subject headings to improve their discoverability by researchers and to increase the productivity of digitization workflows.

Source

Cataloging and classification quarterly. 57(2019) no.5, S.315-336

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