Research Papers
Detecting Family Resemblance: Automated Genre Classification
Authors:
Yunhyong Kim ,
Digital Curation Centre (DCC) & Humanities Advanced Technology Information Institute (HATII), University of Glasgow, Glasgow, UK
Seamus Ross
Digital Curation Centre (DCC) & Humanities Advanced Technology Information Institute (HATII), University of Glasgow, Glasgow, UK
Abstract
This paper presents results in automated genre classification of digital documents in PDF format. It describes genre classification as an important ingredient in contextualising scientific data and in retrieving targetted material for improving research. The current paper compares the role of visual layout, stylistic features, and language model features in clustering documents and presents results in retrieving five selected genres (Scientific Article, Thesis, Periodicals, Business Report, and Form) from a pool of materials populated with documents of the nineteen most popular genres found in our experimental data set.
How to Cite:
Kim, Y. & Ross, S., (2007). Detecting Family Resemblance: Automated Genre Classification. Data Science Journal. 6, pp.S172–S183. DOI: http://doi.org/10.2481/dsj.6.S172
Published on
28 Mar 2007.
Peer Reviewed
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