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.
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.
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 just read until chapter 9, as it was the literature for my class, and I have to say that this book is a really good source for NLP. And I will definitely continue it and use it as a source in the future.
The topics, as complex as they might be, are well explained, often represented within graphics and followed by many examples. There are even some pseudo codes that can be used as a guidance.
A variety of models that are important were shown and compared as of what their advantages and disadvantages are and for what kind of tasks they might be used for. All in all a really good overview.
There are also playlists on YouTube where the most important processes are being explained and shown, which is an efficient addition, if the text alone might be too heavy to read.
Only critic I have so far: some parts are too repetitive. One information is sometimes displayed up to three or four times within the same chapter. As important as it might be, I sometimes got frustrated to reread the same thing again and again. Maybe it just annoyed me because I read chapter to chapter and did not use it as an resourcebook, but well.
+Great book introducing Speech and Language Processing concepts as well as some basic (and more advanced) machine learning applications. +Covers a wide range of applications and methods related to speech and language processing. -Goes into depth on some concepts which might not be common in practice.
This book's reputation as a classic NLP text is well deserved. Numerous diagrams, illustrations, and explanations helped me to understand this bewildering subject. Because this is an academic text book, there are many equations, but few practical examples. For down-and-dirty examples, you might be better served with internet tutorials or the O'Reilly book. But if you want to understand why things work they way they do, this book is great.
Very interesting & useful book for everybody who is interested in natural language processing. I read it when I took online NLP class from prof. Jurafsky at Coursera, and found many interesting things there. I really didn't read it completely, for example, I omitted the speech recognition part, but I plan to return to this book in the near future...
I haven't read this book cover to cover but it's always on my desk and I find myself referring to it at least once a day, and in that, I believe I probably read most of its contents. This is a fundamental reference book for any computational linguist, speech scientist or language data scientist. The explanations and illustrations are very intuitive and not at all boring.
My favourite reference for Natural Language Processing algorithms and applications. Daniel Jurafski has a brilliant way of explaining things! Highly recommend it for anyone looking to make a career in NLP.
It's a great book on NLP, written by leading researchers in the field. In general, I liked it but maybe it could be split into two books, i.e. fundamentals and applications and computational linguistics or something of the sort. Looking at the latest draft, I think Dan and James and heading in that direction already. In any case, this is the Bible of NLP and I strongly recommend at least the first twelve chapters to anyone who is interested in natural language processing.
P.S. One important note is that the field is currently in the midst of a scientific revolution and on the hype train (I'm looking at you ChatGPT), so new research papers come out literally every week. Although Dan and James are trying to keep up, it's not an easy task to do. For example, as I was reading the 2022 draft, the 2023 draft came out (on Jan. 7th). Of course, most of the fundamentals are unchanged but one should keep that in mind and make sure to always read the latest edition of the book (which can be found here: https://web.stanford.edu/~jurafsky/slp3/)
I read this off and on over the past 6 months in self-study. I have a pretty decent math/computational background and like linguistics, but previously had no idea what computational linguistics encompassed, so my goal was to get a good survey of the field. This book was amazing at that - it introduced a ton of concepts, but introduced them clearly and in a well-organized, reader-friendly way.
The more technical sections often didn't have enough context or examples for me to understand properly (i.e. to be able to implement something myself). But it's understandable because this is an intro book, and the chapters always end with a summary of the topics and additional references. For the things that I was more interested in (syntax parsing, semantic parsing), it was easy to learn more deeply. For other sections, I just glossed over/skipped them. :|
An all-inclusive, encylopedic-like textbook on linguistics with an emphasis on the latest developments on speech and language recognition (e.g. computational linguistics, machine translation). I liked the detailed descriptions of various linguistic terms (e.g. prepositions, postpositions, syntactic parsing etc.) and the meticulous summarisation of almost each paragraph on the left margins, but I didn't particularly like/could not follow/could not understand the extensive algebraic fractions and equations. Either it's me not being able to follow equations in general, or they were a bit too much information for such a textbook; I'm not sure...
The holy grail of Natural Language Processing. Includes state of the art algorithms and delves into subjects such as machine learning in detail despite not being it's main focus. It also includes plenty of automata theory which is it's basis for many of the algorithms presented.
Not an easy book by any means and if I had any complaint, is that it's extremely verbose and at the same time extremely technical. So in depth understanding of automata and math is required.
What a nice book to learn both (modern) Natural Language Processing and (computational) Linguistics. I am recommending this book for both undergraduate (but with high level of mathematics) and graduate students.
After some research on NLP related resources, I decided to buy Speech and Language Processing by Daniel Jurafsky & James H. Martin.
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.
This and Foundations of Statistical Natural Language Processing by Manning and Schutze are the two big introductory texts in natural language processing. I prefer the Jurafsky book; it goes into more detail, has more examples, and is written more for use as a class text. Make sure you get the second edition, though.
This is a good introduction to all the major topics of computational linguistics, which includes automatic speech recognition and processing, machine translation, information extraction, and statistical methods of linguistic analysis, among other things.
One of the better computer science titles I have read. Comprehensive and contains enough info on everything to give you a good basic understanding. As little bit of math is required for understanding, but it is a science book after all :-)
My professor chose this as the text book for our computational linguistic course. The book covers all aspects that we discussed in a semester and gives excellent intuitive explanations as well formal derivations. I loved the course and the book