Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
An explosion of Web-based language techniques, merging of distinct fields, availability of phone-based dialogue systems, and much more make this an exciting time in speech and language processing. The first of its kind to thoroughly cover language technology - at all levels and with all modern technologies - this book takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. KEY TOPICS: Builds each chapter around one or more worked examples demonstrating the main idea of the chapter, usingthe examples to illustrate the relative strengths and weaknesses of various approaches. Adds coverage of statistical sequence labeling, information extraction, question answering and summarization, advanced topics in speech recognition, speech synthesis. Revises coverage of language modeling, formal grammars, statistical parsing, machine translation, and dialog processing. MARKET: A useful reference for professionals in any of the areas of speech and language processing.
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... Proper-Noun NP → TWA | Houston NP → Det Nominal NP → Det Nominal
Nominal → Noun Nominal → book | flight | meal | money Nominal → Nominal
Noun Nominal → Nominal Noun Nominal → Nominal PP Nominal → Nominal PP
Each entry predicted in Chart and also to the predictions for the various kinds
of Nominal.The first of these causes SCANNER to be called for the last time to
process the word flight. Finally, moving on to Chart: the presence of the state ...
Consider the following three rules from the last section. VP → VerbNP 〈VP
AGREEMENT〉 = 〈Verb AGREEMENT〉 NP → Det Nominal 〈Det AGREEMENT〉
= 〈Nominal AGREEMENT〉 〈NP AGREEMENT〉 = 〈Nominal AGREEMENT〉
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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.
Words and Transducers
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