Deep Learning–Based Constituency Parsing for Arabic Language

• Published in: Advances in Artificial Intelligence and Applied Cognitive Computing pp. 45 - 58
• Main Authors: Morad, Amr, Nagi, Magdy, Alansary, Sameh
• Format: Book Chapter
• Language: English
• Published: Cham Springer International Publishing 2021
• Series: Transactions on Computational Science and Computational Intelligence
• Subjects: Arabic Language; Constituency Parse Trees; Deep Learning; Natural Language Processing; Workflow
• ISBN: 9783030702953; 3030702952
• ISSN: 2569-7072; 2569-7080
• DOI: 10.1007/978-3-030-70296-0_4

Constituency parse tree is considered the backbone of several Natural Language Processing (NLP) tasks. Deep learning techniques are adopted because they generate parse tree using a dataset without any predefined rules, making them extensible to any language. To capture the semantic meaning, dense words representation technique is necessary. This chapter combines both dense Arabic word representations and deep learning model to generate constituent parse tree. The resultant tree is used in a complete workflow. It contains a web-based application to enable linguists to choose the sentence, generate its constituent, review resultant tree, and edit needed parts. Moreover, the curated output sentence will be used to retrain the model for self-correction. The model is efficient and parallel, resulting in a quick training process.