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

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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Contributor : Laurent Vanni <>
Submitted on : Thursday, May 31, 2018 - 3:56:34 PM
Last modification on : Monday, December 14, 2020 - 6:12:26 PM
Long-term archiving on: : Saturday, September 1, 2018 - 2:42:20 PM


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  • HAL Id : hal-01804310, version 1



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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