Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech Recognition (Prentice Hall Series in Artificial Intelligence)

This book takes an empirical approach to language processing, based on applying statistical and other machine-learning algorithms to large corpora.Methodology boxes are included in each chapter. Each chapter is built around one or more worked examples to demonstrate the main idea of the chapter. Covers the fundamental algorithms of various fields, whether originally proposed for spoken or written language to demonstrate how the same algorithm can be used for speech recognition and word-sense disambiguation. Emphasis on web and other practical applications. Emphasis on scientific evaluation. Useful as a reference for professionals in any of the areas of speech and language processing.

My gut feeling after reading this text is that parsing techniques will likely give way to statistical and probabilistic learning methods that will in some sense bypass the need to correctly or accurately parse language. I cannot fault the authors for not exploring this in more depth,as this represents the cutting edge for both NLP and artificial intelligence. In any case, I'm off to read Schutze and Manning's book which will hopefully provide a bit more focus on that perspective. What intrigues me is that most people can understand some language, but very few people understand the grammar of their own language, especially if they have been deprived of a formal education. So why should computers need to know all about grammar rules and parsing? Could they instead be trained by simply being exposed to enough interactions between language and objects? I teach in a department dominated by both foreign and immigrant students. I understand them most of the time, but I would estimate that half the time their sentences or utterances would not fail to be parsed correctly.


Good oveview, slightly overrated: broad and shallow   May 26, 2002
Peter Alfheim (United States)
28 out of 34 found this review helpful

GENERAL IDEA: Broad coverage, it lacks depth and details - particularly practical details. That is, the presentation is often sketchy, mainly because it approaches too many subjects for its available space. I would not say that this book is strong on theory either. It is quite obvious that it avoids getting too formal and precise, probably to remain attractive for non-specialists too.

CASE STUDY: One specific problem I had with the Hidden Markov Models, that are supperficially presented (or spread I could say) in several separate sections of the book, so it's not been a pleasure trying to actually understand them properly and completely as a fundamental concept, to make them work in my particular application.

TITLE: The book's title IS misleading because it starts with "Speeech" and this book's main subject is not speech but (written) language. Actually there are only a few chapters on speech.

CONCLUSION: Get this book if you are looking for a good overview of the field. The book will introduce you to a thousand of topics. As soon as you need in-depth coverage of some particular topic, you will look for additional resources.


Good, but many errors   May 20, 2002
14 out of 14 found this review helpful

This book is a great general introduction to NLP, covering a broad range of topics. Unfortunately there are many errors in the mathematical formulae and the algorithm descriptions, so do make sure to download the errata list from the book's home page.