Similar Meaning Analysis for Original Documents Identification in Arabic Language

Abstract

The progressive advancement in technology has become easy to present the language expression of someone else as one’s own with similar words semantically. This phenomenon increased the potential source of plagiarism. Its detection is a challenge especially in the case of Arabic paraphrase because of the semantic ambiguity of this language. In recent decades, researches have been hindered by the very limited availability of well-structured datasets. In this context, our main objectives are focused on constructing a corpus for Arabic and presenting thereafter its impact for identifying paraphrase. Indeed, we generated the suspect documents from the Open Source Arabic Corpora (OSAC). Distributed word representation (word2vec) and part-of-speech methods were useful for replacing each original word by its most similar one that had the same grammatical class. Moreover, we captured the structure of Arabic sentences with different window sizes and vector dimensions. Then, we studied how this corpus could be used efficiently in the evaluation of Natural Language Processing (NLP) methods (i.e. Term Frequency-Inverse Document Frequency (TF-IDF), Latent Semantic Analysis (LSA), Latent Dirichlet Allocation (LDA), word2vec, Global Vector Representation (GloVe), and Convolutional Neural Network (CNN)) for paraphrase detection. Experiments revealed which one could outperformed significantly for preserving semantic properties of Arabic words with various linear regularities, alleviating data sparseness and increasing the degree of semantic similarity, in terms of precision and recall.