Using machine learning models to interpret disciplinary styles of metadiscourse in dissertation abstracts

Demarest, Bradford; Sugimoto, Cassidy R.

Permalink

https://hdl.handle.net/2142/42038 Copy

Description

Title

Using machine learning models to interpret disciplinary styles of metadiscourse in dissertation abstracts

Author(s)

• Demarest, Bradford
• Sugimoto, Cassidy R.

Issue Date

2013-02

Keyword(s)

• metadiscourse
• disciplinarity
• machine learning
• support vector model
• dissertation abstracts
• bibliometrics and scholarly communication

Date of Ingest

2013-02-02T05:59:02Z

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.

Publisher

iSchools

Type of Resource

text

Genre of Resource

Conference Poster

Language

en

Permalink

http://hdl.handle.net/2142/42038

DOI

https://doi.org/10.9776/13459

Copyright and License Information

Copyright © 2013 is held by the authors. Copyright permissions, when appropriate, must be obtained directly from the authors.

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