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User Review - Flag as inappropriate 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. Review: Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics and Speech RecognitionUser Review - Mayuri - Goodreadsnice book Read full review Related booksContents
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Common terms and phrasesacoustic algorithm alignment ambiguity applied approach automaton bigram Chapter classifier compute constituent constraints context context-free context-free grammars corpus decoding defined definition dialogue difficult diphone disambiguation discourse documents English evaluation example extraction feature structures field Figure final find finite finite-state first flight frequency given grammar hidden Markov model input introduced labeled language model language processing lexical lexicon likelihood linguistic machine machine learning Markov match meaning representation modified morpheme morphological N-gram natural language node Nominal non-terminal noun phrase parse tree parser part-of-speech tagging predict probabilistic probability problem pronoun reference regular expression regular languages relations represent role rules segmentation semantic sense sentence sequence shows specific speech and language speech recognition string suffix summarization symbols syntactic tagger tagset task training set transducer translation Treebank unification values vector verb Viterbi Viterbi algorithm vowel word WordNet References to this bookFrom Google ScholarFoundations of Statistical Natural Language ProcessingChristopher D Manning, Hinrich Schutze - Computational Linguistics Predicting Subcellular Localization of Proteins using Machine ...Z Lu, D Szafron, R Greiner, P Lu, DS Wishart, B Poulin, J Anvik, C Macdonell, R Eisner - Bioinformatics Anaphora and Discourse StructureBonnie Webber, Matthew Stone, Aravind Joshi, Alistair Knott - 2003 - Computational Linguistics An Improved Error Model for Noisy Channel Spelling CorrectionEric Brill, Robert C Moore Bibliographic information
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