Mastering Text Classification (Hardcover)

This book provides cutting-edge natural language processing (NLP) techniques to unlock the power of text data. It presents advanced methods for various text classification tasks, like discourse relation classification, classification in large taxonomies, and leveraging disagreement between annotators for text classification.

This book equips readers whether they are researchers or professionals, looking to apply NLP in real-world settings, with the latest advancements, and gives them the opportunity to explore techniques to handle limited data, and harness the power of pre-trained language models like BERT. By the end, readers will be equipped to tackle specific text classification challenges and advance the field of NLP.

Dr. Mourad Abbas is a professor at the University of Science and Technology, Houari Boumediene specializing in the dynamic field of natural language processing with a primary focus on the Arabic language and its diverse dialects. With a passion for pushing the boundaries of NLP, Dr. Abbas' research interests span a wide range of crucial topics, including machine translation, speech recognition, language identification, natural language understanding, and the challenges faced by under-resourced languages. Throughout his career, Dr. Abbas has made many contributions to the academic community, having published over sixty impactful papers. He has also played an important role in editing the proceedings of the International Conference on Natural Language and Speech Processing, featured in prestigious platforms like Elsevier, ACL Anthology, and IEEExplore. Recognized as an expert in his field, Dr. Abbas is actively engaged in peer review activities for distinguished journals, such as Language Resources and Evaluation, and Digital Signal Processing, along with conferences like ICASSP, Interspeech, Coling, and NAACL-HLT.