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Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic
Joint Authors
Source
The International Arab Journal of Information Technology
Issue
Vol. 18, Issue 1 (31 Jan. 2021), pp.1-7, 7 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2021-01-31
Country of Publication
Jordan
No. of Pages
7
Main Subjects
Information Technology and Computer Science
Abstract EN
Paraphrase detection allows determining how original and suspect documents convey the same meaning.
It has attracted attention from researchers in many Natural Language Processing (NLP) tasks such as plagiarism detection, question answering, information retrieval, etc., Traditional methods (e.g., Term Frequency-Inverse Document Frequency (TF-IDF), Latent Dirichlet Allocation (LDA), and Latent Semantic Analysis (LSA)) cannot capture efficiently hidden semantic relations when sentences may not contain any common words or the co-occurrence of words is rarely present.
Therefore, we proposed a deep learning model based on Global Word embedding (GloVe) and Recurrent Convolutional Neural Network (RCNN).
It was efficient for capturing more contextual dependencies between words vectors with precise semantic meanings.
Seeing the lack of resources in Arabic language publicly available, we developed a paraphrased corpus automatically.
It preserved syntactic and semantic structures of Arabic sentences using word2vec model and Part-Of-Speech (POS) annotation.
Overall experiments shown that our proposed model outperformed the state-of-the-art methods in terms of precision and recall.
American Psychological Association (APA)
Adnen Mahmoud& Zrigui, Mounir. 2021. Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic. The International Arab Journal of Information Technology،Vol. 18, no. 1, pp.1-7.
https://search.emarefa.net/detail/BIM-1430914
Modern Language Association (MLA)
Adnen Mahmoud& Zrigui, Mounir. Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic. The International Arab Journal of Information Technology Vol. 18, no. 1 (Jan. 2021), pp.1-7.
https://search.emarefa.net/detail/BIM-1430914
American Medical Association (AMA)
Adnen Mahmoud& Zrigui, Mounir. Semantic Similarity Analysis for Corpus Development and Paraphrase Detection in Arabic. The International Arab Journal of Information Technology. 2021. Vol. 18, no. 1, pp.1-7.
https://search.emarefa.net/detail/BIM-1430914
Data Type
Journal Articles
Language
English
Notes
Text in English ; abstracts in .
Record ID
BIM-1430914