Semantic Similarity of Arabic Sentences with Word Embeddings

El Moatez Billah Nagoudi 1, 2 Didier Schwab 3, 1, 2
1 LIG - Laboratoire d'Informatique de Grenoble
2 GETALP - Groupe d’Étude en Traduction Automatique/Traitement Automatisé des Langues et de la Parole
LIG - Laboratoire d'Informatique de Grenoble
3 UGA - Université Grenoble Alpes
Abstract : Semantic textual similarity is the basis of countless applications and plays an important role in diverse areas, such as information retrieval, plagiarism detection, information extraction and machine translation. This article proposes an innovative word embedding-based system devoted to calculate the semantic similarity in Arabic sentences. The main idea is to exploit vectors as word representations in a multidi-mensional space in order to capture the semantic and syntactic properties of words. IDF weighting and Part-of-Speech tagging are applied on the examined sentences to support the identification of words that are highly descriptive in each sentence. The performance of our proposed system is confirmed through the Pearson correlation between our assigned semantic similarity scores and human judgments.
Keywords : Vector Model Space Semantic Sentences Similarity Word Embedding Word Representations
Type de document :
Communication dans un congrès
Third Arabic Natural Language Processing Workshop, Apr 2017, Valencia, France. Third Arabic Natural Language Processing Workshop, pp.18 - 24
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Soumis le : lundi 15 janvier 2018 - 15:43:07
Dernière modification le : mardi 12 février 2019 - 01:31:11
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El Moatez Billah Nagoudi, Didier Schwab. Semantic Similarity of Arabic Sentences with Word Embeddings. Third Arabic Natural Language Processing Workshop, Apr 2017, Valencia, France. Third Arabic Natural Language Processing Workshop, pp.18 - 24. 〈hal-01683485〉

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