This handbook provides a comprehensive understanding of computational linguistics, focusing on the integration of deep learning in natural language processing (NLP). 18 edited chapters cover the state-of-the-art theoretical and experimental research on NLP, offering insights into advanced models and recent applications.
Highlights:
— Foundations of NLP: Provides an in-depth study of natural language processing, including basics, challenges, and applications.
— Advanced NLP Techniques: Explores recent advancements in text summarization, machine translation, and deep learning applications in NLP.
— Practical Applications: Demonstrates use cases on text identification from hazy images, speech-to-sign language translation, and word sense disambiguation using deep learning.
— Future Directions: Includes discussions on the future of NLP, including transfer learning, beyond syntax and semantics, and emerging challenges.
Key Features:
— Comprehensive coverage of NLP and deep learning integration.
— Practical insights into real-world applications
— Detailed exploration of recent research and advancements through 16 easy to read chapters
— References and notes on experimental methods used for advanced readers
Ideal for researchers, students, and professionals, this book offers a thorough understanding of computational linguistics by equipping readers with the knowledge to understand how computational techniques are applied to understand text, language and speech.
Readership
Researchers, students, and professionals in computer science and related fields (AI, ML, NLP and computational linguistics).
