The question we answer with this paper is: ‘can we convert a text document into an image to take advantage of image neural models to classify text documents?’ To answer this question we present a novel text classification method that converts a document into an encoded image, using word embedding. The proposed approach computes the Word2Vec word embedding of a text document, quantizes the embedding, and arranges it into a 2D visual representation, as an RGB image. Finally, visual embedding is categorized with state-of-the-art image classification models. We achieved competitive performance on well-known benchmark text classification datasets. In addition, we evaluated our proposed approach in a multimodal setting that allows text and image information in the same feature space.

Visual Word Embedding for Text Classification

Gallo I.;Nawaz S.;Landro N.;La Grassa R.
2021-01-01

Abstract

The question we answer with this paper is: ‘can we convert a text document into an image to take advantage of image neural models to classify text documents?’ To answer this question we present a novel text classification method that converts a document into an encoded image, using word embedding. The proposed approach computes the Word2Vec word embedding of a text document, quantizes the embedding, and arranges it into a 2D visual representation, as an RGB image. Finally, visual embedding is categorized with state-of-the-art image classification models. We achieved competitive performance on well-known benchmark text classification datasets. In addition, we evaluated our proposed approach in a multimodal setting that allows text and image information in the same feature space.
2021
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
978-3-030-68779-3
978-3-030-68780-9
25th International Conference on Pattern Recognition Workshops, ICPR 2020
-
2021
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11383/2125887
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