title: Zone analysis in biology articles as a basis for information extraction.
abstract: In the field of biomedicine, an overwhelming amount of experimental data has become available as a result of the high throughput of research in this domain. The amount of results reported has now grown beyond the limits of what can be managed by manual means. This makes it increasingly difficult for the researchers in this area to keep up with the latest developments. Information extraction (IE) in the biological domain aims to provide an effective automatic means to dynamically manage the information contained in archived journal articles and abstract collections and thus help researchers in their work. However, while considerable advances have been made in certain areas of IE, pinpointing and organizing factual information (such as experimental results) remains a challenge. In this paper we propose tackling this task by incorporating into IE information about rhetorical zones, i.e. classification of spans of text in terms of argumentation and intellectual attribution. As the first step towards this goal, we introduce a scheme for annotating biological texts for rhetorical zones and provide a qualitative and quantitative analysis of the data annotated according to this scheme. We also discuss our preliminary research on automatic zone analysis, and its incorporation into our IE framework.
authors.affiliation: National Institute of Informatics, 2-1-2 Hitotsubashi, 101-8430 Tokyo, Japan. ymizuta@nii.ac.jp
authors.forename: Yoko
authors.initials: Y
authors.lastname: Mizuta
authors.forename: Anna
authors.initials: A
authors.lastname: Korhonen
authors.forename: Tony
authors.initials: T
authors.lastname: Mullen
authors.forename: Nigel
authors.initials: N
authors.lastname: Collier
mesh_terms: D000043:Abstracting and Indexing / Q000379:methods*; D001185:Artificial Intelligence; D001695:Biology; D003628:Database Management Systems; D016206:Databases, Bibliographic; D016247:Information Storage and Retrieval / Q000379:methods*; D008037:Linguistics; D009323:Natural Language Processing; D010506:Periodicals as Topic; D012660:Semantics; D009626:Terminology as Topic; D018875:Vocabulary, Controlled