Detecting Family Resemblance: Automated Genre Classification
スポンサーリンク
概要
- 論文の詳細を見る
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.
- CODATAの論文
著者
-
Ross Seamus
Digital Curation Centre (DCC) & Humanities Advanced Technology Information Institute (HATII), University of Glasgow
-
Kim Yunhyong
Digital Curation Centre (DCC) & Humanities Advanced Technology Information Institute (HATII), University of Glasgow