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Chapitre d'ouvrage Année : 2020

Using the First Axis of a Correspondence Analysis as an Analytic Tool


Our corpus of medieval French texts is divided into 59 discourse units (DUs) which cross text genres and spoken vs non-spoken text chunks (as tagged with q and sp TEI tags). A correspondence analysis (CA) performed on selected POS tags indicates orality as the main dimension of variation across DUs. Orality prevails over textual features which could fit in a one-dimensional model as well, such as text form (verse vs prose) or time (composition century). We then design several methodological paths to investigate this gradient as computed by the CA first axis. Bootstrap is used to check the stability of observations; gradient-ordered barplots provide both a synthetic and analytic view of the correlation of any variable with the gradient; a way is also found to characterize the gradient poles (here, more-oral or less-oral poles) not only with the POS used for the CA analysis, but also with word forms, in order to get a more accurate and lexical description. This methodology could be transposed to other data with a potential gradient structure.
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halshs-03070182, version 1 (15-12-2020)


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Bénédicte Pincemin, Alexei Lavrentiev, Céline Guillot-Barbance. Using the First Axis of a Correspondence Analysis as an Analytic Tool: Application to Establish and Define an Orality Gradient for Genres of Medieval French Texts. Domenica Fioredistella IEZZI; Damon MAYAFFRE; Michelangelo MISURACA. Text Analytics, 58, Springer International Publishing, pp.127-143, 2020, Studies in Classification, Data Analysis, and Knowledge Organization, 978-3-030-52679-5. ⟨10.1007/978-3-030-52680-1_11⟩. ⟨halshs-03070182⟩
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