Detecting Family Resemblance: Automated Genre Classification

• Main Authors: Yunhyong Kim, Seamus Ross
• Format: Article
• Language: English
• Published: Ubiquity Press 2007-03-01
• Series: Data Science Journal
• Subjects: Automated genre classification; Metadata; Scientific information; Information management; Information extraction
• Online Access: http://datascience.codata.org/articles/405

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.