Argumentative zoning : information extraction from scientific text
We present a new type of analysis for scientific text which we call <i>Argumentative Zoning</i>. We demonstrate that this type of text analysis can be used for generating user-tailored and task-tailored summarises or for performing more informative citation analyses. We also demonstrate that our type of analysis can be applied to unrestricted text, both automatically and by humans. The corpus we use for the analysis (80 conference papers in computational linguistics) is a difficult test bed; it shows great variation with respect to subdomain, writing style, register and linguistic expression. We present reliability studies which we performed on this corpus and for which we used two unrelated trained annotators. The definition of our seven categories (argumentative zones) is not specific to the domain, only to the text type; it is based on the typical argumentation to be found in scientific articles. It reflects the attribution of intellectual ownership in articles, expressions of authorâs stance and typical statements about problem-solving processes. On the basis of sentential features, we use a Naive Bayesian model and an ngram model over sentences to estimate a sentenceâs argumentative status, taking
Teufel, Simone
Argumentative zoning : information extraction from scientific text. We present a new type of analysis for scientific text which we call <i>Argumentative Zoning</i>. We demonstrate that this type of text analysis can be used for generating user-tailored and task-tailored summarises or for performing more informative citation analyses. We also demonstrate that our type of analysis can be applied to unrestricted text, both automatically and by humans. The corpus we use for the analysis (80 conference papers in computational linguistics) is a difficult test bed; it shows great variation with respect to subdomain, writing style, register and linguistic expression. We present reliability studies which we performed on this corpus and for which we used two unrelated trained annotators. The definition of our seven categories (argumentative zones) is not specific to the domain, only to the text type; it is based on the typical argumentation to be found in scientific articles. It reflects the attribution of intellectual ownership in articles, expressions of authorâs stance and typical statements about problem-solving processes. On the basis of sentential features, we use a Naive Bayesian model and an ngram model over sentences to estimate a sentenceâs argumentative status, taking
argumentative information extraction scientific text present_new type analysis scientific text call argumentative type text_analysis use generate user task summarises perform informative citation analysis also type analysis apply unrestricted text automatically human corpus use analysis conference paper computational_linguistics difficult test bed show great variation respect write_style register linguistic_expression present reliability study perform corpus use unrelated train definition category argumentative zone specific domain text_type base typical argumentation find scientific article reflect attribution intellectual ownership article expression author stance typical statement problem_solving process basis sentential feature use naive bayesian model model sentence estimate sentence argumentative status take