Integrates key techniques and technologies of symbolic, statistical, and artificial neural network approaches to natural language processing (NLP)-facilitating the comparison and selection of computational language processing systems.

Comprehensive in scope, this up-to-date Handbook thoroughly explores the design and application of natural language text-based processing systems based on generative linguistics, empirical corpus analysis, and artificial neural networks.

Emphasizes the practical tools and methodologies to accommodate the selected system!

Adopting a historically based structure to the approaches of NLP, each self-contained section of the Handbook of Natural Language Processing provides

• an introductory overview of the approach discussed
• details of fundamental concepts and procedures appropriate to the approach
• specific applications where the approach has been used successfully

With over 2600 references, drawings, photographs, equations, and tables, the Handbook of Natural Language Processing is a must-have reference for anyone working in natural language processing, including electrical and electronic systems, computer, signal processing, communications, and natural language engineers; computer scientists and information technologists; computational, theoretical, and historical linguists; artificial intelligence and language technology software developers; cognitive scientists; and upper-level undergraduate and graduate students in natural language processing courses.