Abstract
This chapter studies the use of textual features based on systemic functional linguistics, for genre-based text categorization. We describe feature sets that represent different types of conjunctions and modal assessment, which together can partially indicate how different genres structure text and may prefer certain classes of attitudes towards propositions in the text. This enables analysis of large-scale rhetorical differences between genres by examining which features are important for classification. The specific domain we studied comprises scientific articles in historical and experimental sciences (paleontology and physical chemistry, respectively). We applied the SMO learning algorithm, which with our feature set achieved over 83% accuracy for classifying articles according to field, though no field-specific terms were used as features. The most highly-weighted features for each were consistent with hypothesized methodological differences between historical and experimental sciences, thus lending empirical evidence to the recent philosophical claim of multiple scientific methods.
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Argamon, S., Dodick, J. (2006). Corpus-Based Study of Scientific Methodology: Comparing the Historical and Experimental Sciences. In: Shanahan, J.G., Qu, Y., Wiebe, J. (eds) Computing Attitude and Affect in Text: Theory and Applications. The Information Retrieval Series, vol 20. Springer, Dordrecht. https://doi.org/10.1007/1-4020-4102-0_17
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DOI: https://doi.org/10.1007/1-4020-4102-0_17
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