Foundations of Statistical Natural Language Processing

Statistical approaches to processing natural language text have become dominant in recent years. This foundational text is the first comprehensive introduction to statistical natural language processing (NLP) to appear. The book contains all the theory and algorithms needed for building NLP tools. It provides broad but rigorous coverage of mathematical and linguistic foundations, as well as detailed discussion of statistical methods, allowing students and researchers to construct their own implementations. The book covers collocation finding, word sense disambiguation, probabilistic parsing, information retrieval, and other applications.

• Speech and Language Processing, 2nd Edition
• Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit
• Introduction to Information Retrieval
• Neural Network Methods in Natural Language Processing (Synthesis Lectures on Human Language Technologies)
• Deep Learning (Adaptive Computation and Machine Learning)
• Pattern Recognition and Machine Learning (Information Science and Statistics)
• The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
• Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning)
• Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
• Taming Text: How to Find, Organize, and Manipulate It