Identifying Sections in Scientific Abstracts using Conditional Random Fields

Paper: Identifying Sections in Scientific Abstracts using Conditional Random Fields

ACL ID	I08-1050
Title	Identifying Sections in Scientific Abstracts using Conditional Random Fields
Venue	International Joint Conference on Natural Language Processing
Session	Main Conference
Year	2008
Authors	Kenji Hirohata (University of Manchester, Manchester UK; National Center for Text Mining, UK) Naoaki Okazaki (University of Tokyo, Tokyo Japan) Sophia Ananiadou Mitsuru Ishizuka

using Conditional Random Fields Kenji Hirohatay hirohata@nii.ac.jp Naoaki Okazakiy okazaki@is.s.u-tokyo.ac.jp Sophia Ananiadouz sophia.ananiadou@manchester.ac.uk yGraduate School of Information Science and Technology, University of Tokyo 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656, Japan Mitsuru Ishizukay ishizuka@i.u-tokyo.ac.jp zSchool of Computer Science, University of Manchester National Centre for Text Mining (NaCTeM) Manchester Interdisciplinary Biocentre, 131 Princess Street, Manchester M1 7DN, UK Abstract OBJECTIVE: The prior knowledge about the rhetorical structure of scientific abstracts is useful for various text-mining tasks such as information extraction, information re- trieval, and automatic summarization. This

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