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Introduction
Arabic is listed as one of the top five world's most popular languages and one of the six official languages of the United Nations. More than 280 million people use it as a first language while at least 250 million use it as a second language over 58 countries. More than 219 million Arabic Internet users are expressing the diversity of different Arabic writings and language cultural, which make the research on the Arabic language very important. Besides, Arabic is known for having a very complex morphology, structure, and its inflectional and derivational nature, which create more challenges to process the language.
Machine Learning (ML) and Deep Learning (DL) methods achieved significant improvements in tackling various natural language processing (NLP) tasks, such as sentiment analysis, question answering, and machine translation. Moreover, in an increasingly globalized world, applications dedicated to cover, investigate, and analyses the morphology and structure complexity of Arabic language using NLP methods are needed.
This book focuses on developing and improving ML and DL methods using Metaheuristic Algorithms (MA) for Arabic NLP tasks rather than directly using previous successful models that have been applied to other languages. The aim is providing the Arabic research community with the most advanced frameworks used various Arabic NLP tasks optimization while examining the theory and application of MA. In this sense, topics are selected based on their importance and complexity in this field. For example, semantic representations, sentiment analysis, question answering, text summarization, and neural machine translation.
Submission Guidelines
All chapters must be original and not simultaneously submitted to another book, journal, or conference. The following chapter categories are welcome but are not limited to the following:
Arabic Dialect Modeling
Authorship Identification and Verification
Chunking and Tokenization
Data Resources, Techniques, Tools and Evaluation
Deep learning
Dialogue and Interactive Systems
Evolutionary Computation Algorithms and Optimization
Information Extraction and Retrieval
Language Models
Lexical and Compositional Semantics
Machine Learning
Machine Translation Morphological Analysis
Metaheuristic Algorithms for Arabic NLP tasks
Multilinguality and Cross-linguality
Named Entity Recognition - Natural Language Generation
Negation and Natural Language Inference
Part of Speech Tagging - Question Answering
Sentiment Analysis and Opinion Mining
Sequence Labeling
Syntactic and Semantic Parsing
Text and Web Content Mining
Text Clustering, and Classification - Text Summarization
Transliteration, Transcription and Diacritization
Word Sense and Syntactic Disambiguation
Important Dates
Deadline for Chapter Submission : 15-5-2019
First decision : June 2019
Camera-ready Submission : 15-7-2019
Expected Publication date : 4th quarter of 2019
For preparing your chapter, kindly, consider the following points:
The publication in this book is free of charge.
Each book chapter should be 20-30 pages.
Each chapter should have an abstract and keywords.
Chapter should conform to the standard guidelines of the Springer’s book chapter format (Template Link)
Publication
RANLP2019 proceedings will be published in in the book series Studies in Computational Intelligence by Springer.
http://www.springer.com/series/7092
Indexing
The book and its chapters are submitted to indexing to Web of Science, EI-Compendex, DBLP, SCOPUS, Google Scholar and Springerlink.
Book Editors
Mohamed Abd El Aziz, Ph.D.
Mathimatics department, Faculty of Science, Zagazig Univeristy, Zagazig, Egypt.
[email protected]
Mohammed Al-qaness, Ph.D.
Luoshi road, Wuhan University, School of Computer Science, 430072, Wuhan, China.
[email protected]
Ahmed A. Ewees, Ph.D.
Computer Department, Damietta University, Damietta, Egypt
[email protected], [email protected]
Abdelghani Dahou
Wuhan University of Technology, School of Computer Science and Technology, 430070, Wuhan, China.
[email protected]
Contact
All questions about submissions should be emailed to any of these emails
[email protected], [email protected], [email protected], or [email protected]
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