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Feature Type Analysis in Automated Genre Classification

By Dr Yunhyong Kim and Seamus Ross

Abstract

In this paper, we compare classifiers based on language model, image, and stylistic features for automated genre classification. The majority of previous studies in genre classification have created models based on an amalgamated representation of a document using a multitude of features. In these models, the inseparable roles of different features make it difficult to determine a means of improving the classifier when it exhibits poor performance in detecting selected genres. By independently modeling and comparing classifiers based on features belonging to three types, describing visual, stylistic, and topical properties, we demonstrate that different genres have distinctive feature strengths.

Topics: M Resource Discovery, LA Ingest, EA Metadata
Year: 2007
OAI identifier: oai:eprints.erpanet.org:128

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