Book Review: Foundations of Statistical Natural Language Processing

This book published 20 years ago, but the content still new to me. Statistical NLP is the most interdisciplinary in my view, it involves Linguistics, Computer Science, Statistics, Information Theory, even Philosophy and Neurosciences if you want to know more about NLP

Linguistics kill Philosophy and initiate Artificial Intelligence, that’s why I read this book and want to learn something about NLP, another reason is because I want to start some businesses about English language teaching someday, know something about NLP is necessary to me, and Foundations of Statistical Natural Language Processing is a great choice to me

Part I lays out the mathematical and linguistic foundation that the other parts build on. Concepts and techniques introduced here are referred to throughout the book.
Part II covers word-centered work in Statistical NLP. There is a natural progression from simple to complex linguistic phenomena in its four chapters on collocations, n-gram models, word sense disambiguation, and lexical acquisition, but each chapter can also be read on its own.
The four chapters in part III, Markov Models, tagging, probabilistic context free grammars, and probabilistic parsing, build on each other, and so they are best presented in sequence. However, the tagging chapter can be read separately with occasional references to the Markov Model chapter.
The topics of part IV are four applications and techniques: statistical alignment and machine translation, clustering, information retrieval, and text categorization. Again, these chapters can be treated separately according to interests and time available, with the few dependencies between them marked appropriately.

This book is leading authored by Chris Manning, I followed him on Twitter for a while, but until now, I know him more:

Christopher Manning is the inaugural Thomas M. Siebel Professor in Machine Learning in the Departments of Computer Science and Linguistics at Stanford University. His research goal is computers that can intelligently process, understand, and generate human language material. Manning is a leader in applying Deep Learning to Natural Language Processing, with well-known research on Tree Recursive Neural Networks, sentiment analysis, neural network dependency parsing, the GloVe model of word vectors, neural machine translation, and deep language understanding. He also focuses on computational linguistic approaches to parsing, robust textual inference and multilingual language processing, including being a principal developer of Stanford Dependencies and Universal Dependencies. Manning has coauthored leading textbooks on statistical approaches to Natural Language Processing (NLP) (Manning and Schütze 1999) and information retrieval (Manning, Raghavan, and Schütze, 2008), as well as linguistic monographs on ergativity and complex predicates. He is an ACM Fellow, a AAAI Fellow, and an ACL Fellow, and a Past President of the ACL. Research of his has won ACL, Coling, EMNLP, and CHI Best Paper Awards. He has a B.A. (Hons) from The Australian National University and a Ph.D. from Stanford in 1994, and he held faculty positions at Carnegie Mellon University and the University of Sydney before returning to Stanford. He is the founder of the Stanford NLP group (@stanfordnlp) and manages development of the Stanford CoreNLP software.

A talk between Christopher Manning and gYann LeCun:

And you can find an online lectures about Natural Language Processing with Deep Learning (winter 2017) by Christopher Manning (18 lectures totally):