Argumentative Zoning: Information Extraction from Scientific Articles (RRID:SCR_006303)Copy Citation Copied
URL: http://www.cl.cam.ac.uk/~sht25/az.html
Proper Citation: Argumentative Zoning: Information Extraction from Scientific Articles (RRID:SCR_006303)
Description: Simone Teufel''s thesis introducing a rhetorical-level analysis of scientific research articles called Argumentative Zoning that aims to provide an intelligent library search tool for researchers. This could include the summarization of single or multiple articles, and also improved citation indexes. A construct called a citation maps could help people grasp relationships between papers. The definition of the argumentative Zones is given by the sentential-rhetorical speech act of single, important sentences (landmark sentences, e.g. in the paper they develop a method for or in contrast to REFERENCE, their approach uses ...). That means, she is particularly interested in the rhetorical status of that sentence with respect to the communicative function of the whole paper. The zones, each associated with one or more sentences, are the following: * BKG: General scientific background (yellow) * OTH: Neutral descriptions of other people''s work (orange) * OWN: Neutral descriptions of the own, new work (blue) * AIM: Statements of the particular aim of the current paper (pink) * TXT: Statements of textual organization of the current paper (in chapter 1, we introduce...) (red) * CTR: Contrastive or comparative statements about other work; explicit mention of weaknesses of other work (green) * BAS: Statements that own work is based on other work (purple) The thesis looks at how humans perform the analysis, how much they agree or disagree (they agree pretty much!) and how an automatic, rather shallow process can apply the analysis, based on machine learning and sentential features.
Abbreviations: Argumentative Zoning
Resource Type: narrative resource, thesis, data or information resource
Keywords: annotation, text analysis, summarization, computational linguistics, text mining
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