Abstract
ABSTRACTThis paper presents the methodology of a distributional analysis for the exploration of semantic similarity in a small technical corpus. The aim is to show the semantic issues at stake in a series of statistical experiments. First-order co-occurrences of a technical node are clustered based on shared-order second and third-order co-occurrences and on the respective association strength. The results of the statistical clustering techniques show semantic similarities and dissimilarities between first-order co-occurrences by means of proximities and distances on 2D-plots and 3D-plots. By fine-tuning the statistical analysis and by using enriched distributional data, we aim at achieving more revealing semantic interpretations.
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