Abstract
The main challenge of paraphrase is how to detect the semantic relationship between the suspect text document and the source text document. Nowadays, the combination of Natural Language Processing NLP and deep learning based approaches have a booming in the field of text analysis, including: text classification, machine translation, text similarity detection, etc. In this context, we proposed a deep learning based method to detect Arabic paraphrase composed by the following phases: First, we started with a preprocessing phase by extracting the relevant information from text document. Then, word2vec algorithm was used to generate word vectors representation which they would be combined subsequently to generate a sentence vectors representation. Finally, we used a Convolutional Neural Network CNN to improve the ability to capture statistical regularities in the context of sentences which then makes it possible to facilitate the similarity measurement operation between the representations of source and suspicious sentences. The evaluation of our proposed approach gave us a promising result in term of precision.
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Mahmoud, A., Zrigui, A., Zrigui, M. (2018). A Text Semantic Similarity Approach for Arabic Paraphrase Detection. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2017. Lecture Notes in Computer Science(), vol 10762. Springer, Cham. https://doi.org/10.1007/978-3-319-77116-8_25
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DOI: https://doi.org/10.1007/978-3-319-77116-8_25
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