Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition

As its lengthy subtitle suggests, this is a big book (just under a thousand pages) and unbelievably comprehensive. On the whole, the book is a major improvement over its predecessor. The first edition was plagued with typos on seemingly every page, and was also way too thin in certain places. I seem to remember them rushing through phonetics in a single page or two, and then describing optimality theory in just a couple sentences! The second edition's coverage of the field is significantly broader and deeper. Phonetics now gets a good 15 pages. The typos are gone and the appearance of the book is also much improved, with nice-looking black-and-white diagrams on nearly every page. I have one pedagogical quibble with the new edition. The first edition introduced readers to the Bayesian noisy channel model by applying it to the problem of spelling correction, as implemented in the classic paper by Kernighan et al. Because noisy channel spelling correction is so fiendishly simple, and the paper is so readable, this was the perfect way to introduce a student to Bayesian models of language. In the second edition, however, the authors decided to jump straight into noisy channel POS tagging, a much more challenging topic, and to relegate spelling correction to an "Advanced" (?) section at the end of Chapter 5. They really should have started with spelling correction and then moved to tagging. Quibbles aside, this book is a spectacular achievement. The first edition of Speech and Language Processing was a breathtaking synthesis of material, and it helped to unify the field of language technology, despite its flaws. This greatly updated second edition is a big improvement and will be the standard text in the field for years to come.

I read the first edition of that book and it is terrific. The second edition is much more adapted to current research. Statistical methods in NLP are more detailed and some syntax-based approaches are presented. My specific interest is in machine translation and dialogue systems. Both chapters are extensively rewritten and much more elaborated. I believe this book is perfect for everyone who starts in speech and language processing. With precision, coherent examples and some humor, this book give a great introduction into this topic as well as material for already experienced readers.

This book is a feat for anybody interested in Natural Language Processing and probably the most comprehensive book on this subject. It provides an in-depth overview of the most important aspects of NLP from regular expressions to sense disambiguation, discourse, and machine translation. I particularly like the bibliographical and historical notes in each chapter, which provide additional historical context and lots of references. The book is well written and carefully structured. However, it contains several silly typos (real-word errors) that are a bit embarrassing, considering the topic of the book. This book does not cover the hardware components of speech recognition. It only provides an introduction to the computational aspects. Nevertheless, I don't think the title is misleading (as other reviewers claim), but the back-cover should mention that it doesn't cover the electronic and signal processing components of speech recognition.

I started reading James Allen's Natural Language Understanding to get background information on an NLP indepedent study project. The book was good, but I still found some points unclear and turned to Jurafsky/Martin for more information. In the end I found Jurafsky very comprehensive and much more down to earth than Allen. (They make useful references to popular movies and culture without sacrificing their academic reputation.) The work introduces basic NLP concepts as Allen does, but then presents applications that continually refer back to the methods. For example, Allen explains the Viterbi algorithm as a method for tagging sentences. Jurafsky/Martin present it, then refer to it in applications such as spell checking, voice recognition, and sentence tagging. The book also serves as a useful guide to finding the more significant NLP papers for further research. If you're interested in NLP this is an excellent place to start!

The previous best book on NLP was James Allen's (1995), which was considered ambitious at the time because it covered syntax, semantics and some pragmatics. But Martin and Jurafsky is far more ambitious, because it covers speech recognition as well, and has far expanded coverage of language generation and translation. It also covers the great advances in statistical techniques that have marked the last decade. It is a beautiful synthesis that will reward the experienced expert in the field with new insights and new connections in the form of historical notes that are not well known. And it is well-written and clear enough that even the beginning student can follow it through. Before this book, you would have had to read Allen's book, Charniak's short book on statistical NLP, something on speech recognition, and something else on generation and translation. Like squeezing clowns into a circus car, Jurafsky and Martin somehow, improbably, manage to squeeze this all into one book, but in a way that is elegant and holds together perfectly; not at all the hodge-podge that one might expect. I expect that this book will be seen as one of the landmarks that pushes the field forward.It's worth comparing this book to the other recent NLP text: Manning and Shutze. Jurafsky and Martin cover much more ground, including many aspects that are ignored by Manning and Schutze. So if you want a general overview of natural language, if you want to know about the syntax of English, or the intricacies of dialog, if you are teaching or taking a general NLP course, then Jurafsky and Martin is the one for you. But if your needs are more focused on the algorithms for lower-level text processing with statistical techniques, or if you want to build a specific practical application, then Manning and Schutze is far more comprehensive and likely to have your answer. If you're a serious student or professional in NLP, you just have to have both.