Jurafsky Martin Speech Language Processing 2nd Edition
This book offers a unified vision of speech and language processing, presenting state-of-the-art algorithms and techniques for both speech and text-based processing of natural language. This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and This book offers a unified vision of speech and language processing, presenting state-of-the-art algorithms and techniques for both speech and text-based processing of natural language. This comprehensive work covers both statistical and symbolic approaches to language processing; it shows how they can be applied to important tasks such as speech recognition, spelling and grammar correction, information extraction, search engines, machine translation, and the creation of spoken-language dialog agents. The following distinguishing features make the text both an introduction to the field and an advanced reference guide. UNIFIED AND COMPREHENSIVE COVERAGE OF THE FIELD Covers the fundamental algorithms of each field, whether proposed for spoken or written language, whether logical or statistical in origin. EMPHASIS ON WEB AND OTHER PRACTICAL APPLICATIONS Gives readers an understanding of how language-related algorithms can be applied to important real-world problems.
EMPHASIS ON SCIENTIFIC EVALUATION Offers a description of how systems are evaluated with each problem domain. EMPERICIST/STATISTICAL/MACHINE LEARNING APPROACHES TO LANGUAGE PROCESSING Covers all the new statistical approaches, while still completely covering the earlier more structured and rule-based methods.
Jurafsky provides a solid foundational knowledge for computational linguistic - it introduces linguistics, computer science and statistics at comprehensive depth. Some of the major concepts for anybody who wants to know about computational linguistic. Language Model: the book introduces basic models and algorithms evolved around linguistics. There are two major approaches: 1) statistics-based parsing 2) linguistic-based parsing. In practice, most models are built with mixture of both. Part-of-Spee Jurafsky provides a solid foundational knowledge for computational linguistic - it introduces linguistics, computer science and statistics at comprehensive depth. Some of the major concepts for anybody who wants to know about computational linguistic.
Language Model: the book introduces basic models and algorithms evolved around linguistics. There are two major approaches: 1) statistics-based parsing 2) linguistic-based parsing.
In practice, most models are built with mixture of both. Part-of-Speech tagging: a task of labelling words into certain category. This challenge relies on language models - N-Grams, and Hidden Markov Model. Hidden Markov Model (HMM): it's the foundation for many computational linguistic task. The core concept is that language has an extrinsic state (the words we see) and an intrinsic state (syntax and semantic structure behind the sentence), and by keeping track of intrinsic state we can have a better understanding of extrinsic states. Specifically, there are major Dynamic Algorithms such as Forward Algorithm and Viterbi Algorithm that use HMM to classify and predict language models. Context-Free Grammar: put it simply, it's the syntax tree for human language as Abstract Syntax Tree (AST) for computer language, although there are more nuisances as human language is less structured.
Given a sentence, there are 'top-down' and 'bottom-up' algorithms that generate a parse tree. Probabilistic Context-Free Grammar: most times sentences have multiple parse tree candidate, if we have a pre-trained corpus, we can then assign probability to each candidate tree, the book also talks about algorithms to do that. Discourse analysis: two major tasks have been introduced - anaphora resolution (ie. What does 'it' refer to?) and speech coherence. Idioms have been established around how to resolve anaphora by using salience value (recency, subject, object), and how to analyze coherence (Rhetoric Structure Theory) Dialogue System: this is the chapter that interest me the most.
The author talks about the turn-taking nature of dialogue and maxims to follow when developing a dialogue system. Many technical specifications can be implemented using tricks covered in previous chapters. Machine Translation: most machine translation models are statistical. Two major factors to integrate: word occurrence (How like is source word S to be translated to target word T?) and alignment (how likely is source word S to be in the same order as target word T?). On top, we use Bayes rules to calculate probabilities. All in all, it's a comprehensive book, I really enjoyed it.
There are some new chapters coming out of the new edition which I like better. I found this book comprehensive but incomprehensible, primarily because of the lack of real-world examples. It would benefit from a series of programming exercises with training sets made available through a web site. This would tie the extremely abstract algorithms to technology and data. If you are a strong computer scientist with lots of experience in abstract algorithms, this book should give you what you need.
Speech And Language Processing Pdf
If you are interested in producing real-world software systems to do serious spee I found this book comprehensive but incomprehensible, primarily because of the lack of real-world examples. It would benefit from a series of programming exercises with training sets made available through a web site. This would tie the extremely abstract algorithms to technology and data. If you are a strong computer scientist with lots of experience in abstract algorithms, this book should give you what you need. If you are interested in producing real-world software systems to do serious speech and language work, start here but be prepared to go elsewhere for practical tools, methods, and advice about implementation. It's instructive to look at Jurafsky and Martin's Coursera online course based on this book-much more practical and hands on. The book doesn't help much when you take that course.
After some research on NLP related resources, I decided to buy Speech and Language Processing by Daniel Jurafsky & James H. It is quite the door stopper. As usual when buying a textbook, I hoped the book would serve as an introduction, when reading it for the first time, and as a reference for later. Having just finished it, I will only speak about the qualities of the book as an introductory text. Let me get the obvious out of the way: the book is massive. Yet due to the broad scope After some research on NLP related resources, I decided to buy Speech and Language Processing by Daniel Jurafsky & James H.
It is quite the door stopper. As usual when buying a textbook, I hoped the book would serve as an introduction, when reading it for the first time, and as a reference for later. Having just finished it, I will only speak about the qualities of the book as an introductory text. Let me get the obvious out of the way: the book is massive. Yet due to the broad scope I found it wanting in places. The chapter about machine translation is only 50 pages long and barely covers the distinction between statistical and rule based machine translation.
This pattern repeats throughout. In my eyes the chapters ended just as the content started to be truly interesting. Another example - the chapter on speech synthesis covers concatenative waveform synthesis only and mentions formant and articulatory synthesizers only in passing in the chapter notes. The book is divided into five parts. Part one, Words, introduces regular expressions, finite state automata, finite state transducers, part of speech tagging, the n-gram, Hidden Markov and Maximum Entropy models. For me this part of the book was a very good refresher, though I cannot imagine how well it reads for someone unfamiliar with the concepts. Speech, is the second part and it focuses on audio production and analysis: phonetics, speech synthesis, speech recognition and computational phonology.
My biggest complaint about this part of the book is that the Arpabet was used for human-readable examples (instead of the more common IPA). The chapter about phonetics seemed like a good entry point, and speech synthesis covers concatenative waveform synthesis well enough. The chapter about computational phonology (the study on how sounds are realized) was very interesting, though underdeveloped.
In the chapters on speech recognition the authors introduce the noisy channel model and the usage of Gaussian Mixture Models, Hidden Markov Models and Viterby's algorithm for speech recognition. Various means of feature engineering for speech recognition are also shown (windowing, DFT, cepstrum etc).
The first chapter ends with training and evaluation of speech recognition systems, while the second (Advanced Topics) talks about multi-pass and stack based (A.) decoding algorithms, as well as advanced acoustic models, pronunciation variation and speaker-specific optimizations. The third part, Syntax, focuses on formal grammars of English, syntactical parsing, statistical parsing, and the analysis of language and complexity.
The authors show Chomsky's hierarchy, formal grammars and grammar rules for English, as well as an analysis of the English language in the context of the pumping lemma. The chapter on syntactical parsing presents the CKY, Earley and Chart parsing algorithms, defines the problem of top-down and bottom-up parsing and partial parsing. The next chapter is natural extensions with statistical parsing methods and probabilistic context free grammars being covered. Fourth part, Semantics and Pragmatics delves into representations of knowledge. The information presented in this part of the book was hard for me to grasp as it introduced a lot of new concepts from (non-computational) linguistics. The authors described computational semantics - methods of reasoning about meaning representation, lexical semantics - the analysis of relations of meanings e.g hyponymy: a relationship between something general and it's specific instance (dog - german shephard).
A chapter on computational lexical semantics introduces the problem of word sense disambiguation. One of the more involved parts of the book which I'll surely need to reread. The fifth and last part was the most exiting as it showcases some of the most 'flashy' NLP applications: information extraction; question answering and summarization; dialogue and conversational agents; machine translation. I understand that expanding each of those concepts could fill a book by itself. Yet I still felt cheated by the sparsity of the content. The book is written in a very clear and concise way. It's easy to underappreciate good formatting and organization in a book when a book is organized as well as this one.
Typographical rules are introduced and used consistently enhancing the reading experience. Each chapter ends with bibliographical notes and exercises for the readers. While the notes are great I cannot say the same about the exercises.
I really hate textbooks that have exercises without answers, and very loose questions. All in all Speech and Language processing was a pleasant, if taxing, read. As an introductory text - it's great but the broad yet shallow scope makes me think that it will be insufficient as a reference book.
For undergraduate or advanced undergraduate courses in Classical Natural Language Processing, Statistical Natural Language Processing, Speech Recognition, Computational Linguistics, and Human Language Processing. 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 text takes an empirical approach to the subject, based on applying statistical and other machine-learning algorithms to large corporations. The authors cover areas that traditionally are taught in different courses, to describe a unified vision of speech and language processing. Emphasis is on practical applications and scientific evaluation. An accompanying Website contains teaching materials for instructors, with pointers to language processing resources on the Web.
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The Second Edition offers a significant amount of new and extended material. Supplements: Click on the 'Resources' tab to View Downloadable Files:. Solutions. Power Point Lecture Slides - Chapters 1-5, 8-10, 12-13 and 24 Now Available!. For additional resourcse visit the author website: http://www.cs.colorado.edu/martin/slp.html. Dan Jurafsky is an associate professor in the Department of Linguistics, and by courtesy in Department of Computer Science, at Stanford University.
Previously, he was on the faculty of the University of Colorado, Boulder, in the Linguistics and Computer Science departments and the Institute of Cognitive Science. He was born in Yonkers, New York, and received a B.A. In Linguistics in 1983 and a Ph.D. In Computer Science in 1992, both from the University of California at Berkeley.
He received the National Science Foundation CAREER award in 1998 and the MacArthur Fellowship in 2002. He has published over 90 papers on a wide range of topics in speech and language processing. Martin is a professor in the Department of Computer Science and in the Department of Linguistics, and a fellow in the Institute of Cognitive Science at the University of Colorado at Boulder. He was born in New York City, received a B.S. In Comoputer Science from Columbia University in 1981 and a Ph.D.
In Computer Science from the University of California at Berkeley in 1988. He has authored over 70 publications in computer science including the book A Computational Model of Metaphor Interpretation.