Recent Advances in NLP: The Case of Arabic Language

In light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Each chapter addresses a common challenge in a theoretical or applied aspect of intelligent natural language processing related to Arabic language. Many challenges encountered during the development of the solutions can be resolved by incorporating language technology and artificial intelligence.

The topics covered include machine translation; speech recognition; morphological, syntactic, and semantic processing; information retrieval; text classification; text summarization; sentiment analysis; ontology construction; Arabizi translation; Arabic dialects; Arabic lemmatization; and building and evaluating linguistic resources.

This book is a valuable reference for scientists, researchers, and students from academia and industry interested in computational linguistics and artificial intelligence, especially for Arabic linguistics and related areas.

Focuses on developing and improving deep learning methods and architectures using metaheuristic algorithms (MA) for Arabic NLP tasks Presents a compendium of recent computational methods and techniques to address the problems of various Arabic NLP tasks Discusses recent advances in nature language processing, focusing on Arabic language

Inhalt
Text summarization: A brief review.- Single Arabic Document Summarization Using Natural Language Processing Technique.- Proposed Natural Language Processing Preprocessing Procedures for Enhancing Arabic Text Summarization.- Effects of Stemming on Feature Extraction and Selection for Arabic Documents Classification.- Improving Arabic Lemmatization through a lemmas database and a machine-learning technique.- The role of transliteration in the process of Arabizi translation/sentiment analysis.- Sentiment analysis in healthcare: A brief review.- Aspect-Based Sentiment Analysis for Arabic Government Reviews.- Prediction of the Engagement Rate on Algerian Dialect Facebook Pages.- Predicting Quranic Audio Clips Reciters Using Classical Machine Learning Algorithms: A Comparative Study.