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Title:Using machine learning models to interpret disciplinary styles of metadiscourse in dissertation abstracts
Author(s):Demarest, Bradford; Sugimoto, Cassidy R.
Subject(s):metadiscourse
disciplinarity
machine learning
support vector model
dissertation abstracts
bibliometrics and scholarly communication
Abstract:This paper presents the results of a study of disciplinary stylistic differences among dissertation abstracts from physics, psychology, and philosophy. Based on differences in relative frequencies of metadiscourse terms as provided by Hyland (2005), we used a machine learning approach to construct SMO vector support models of each discipline whose average accuracy (88.3%) surpassed a baseline model by 22%. We found that model term weights supported the findings of previous qualitative research regarding differences between disciplines and by extension between hard sciences, social sciences, and humanities. Given the success of the metadiscourse-based model, we conclude by proposing an expanded study to investigate disciplinary style both across disciplines and over time.
Issue Date:2013-02
Publisher:iSchools
Citation Info:Demarest, B. & Sugimoto, C. R. (2013). Using machine learning models to interpret disciplinary styles of metadiscourse in dissertation abstracts. iConference 2013 Proceedings (pp. 901-904). doi:10.9776/13459
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/42038
DOI:10.9776/13459
Publication Status:published or submitted for publication
Peer Reviewed:is peer reviewed
Rights Information:Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.
Date Available in IDEALS:2013-02-02


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