Text Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis

Laurent Vanni 1 Mélanie Ducoffe 2 Damon Mayaffre 1 Frédéric Precioso 2 Dominique Longrée 3 Veeresh Elango 2 Nazly Santos Buitrago 2 Juan Gonzales Huesca 2 Luis Galdo 2 Carlos Aguilar 4
1 BCL, équipe Logométrie : corpus, traitements, modèles
BCL - Bases, Corpus, Langage (UMR 7320 - UNS / CNRS)
2 Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD
Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
3 LASLA - Laboratoire d'Analyse Statistique des Langues Anciennes
4 BCL, équipe Langage et Cognition
BCL - Bases, Corpus, Langage (UMR 7320 - UNS / CNRS)
Abstract : In this paper, we propose a new strategy , called Text Deconvolution Saliency (TDS), to visualize linguistic information detected by a CNN for text classification. We extend Deconvolution Networks to text in order to present a new perspective on text analysis to the linguistic community. We empirically demonstrated the efficiency of our Text Decon-volution Saliency on corpora from three different languages: English, French, and Latin. For every tested dataset, our Text Deconvolution Saliency automatically encodes complex linguistic patterns based on co-occurrences and possibly on grammatical and syntax analysis.
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Laurent Vanni, Mélanie Ducoffe, Damon Mayaffre, Frédéric Precioso, Dominique Longrée, et al.. Text Deconvolution Saliency (TDS) : a deep tool box for linguistic analysis. 56th Annual Meeting of the Association for Computational Linguistics, Jul 2018, Melbourne, France. 〈hal-01804310〉

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