An Introduction to Language Processing with Perl and Prolog: An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German (Cognitive Technologies) Review

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An Introduction to Language Processing with Perl and Prolog: An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German (Cognitive Technologies) ReviewThis book has the same scope as Speech and Language Processing (2nd Edition) (Prentice Hall Series in Artificial Intelligence), but that book flies too high over the details. This book does a good job of covering the details without getting lost in them. The author takes the approach of explaining a concept first with excellent illustrations, then explains the algorithm that implements the concept, then shows detailed code in either Prolog or PERL.
This is not to say you can pick up this book without the proper background and get much out of it. Language processing is a field requiring a good background in a number of fields including the theory of computation, linguistics, artificial intelligence, and information theory to name a few. You should be familiar with all of these fields before tackling the book, although the author does introduce these topics somewhat before digging into details.
One thing the author does not do much of is explain Prolog or PERL. He assumes you already know these languages, although there is an appendix at the back of the book covering Prolog. Prolog is a difficult language to learn and is not at all intuitive. However, it is an excellent choice for coding up many algorithms concerning artificial intelligence. Thus, although I do not argue with the author's choice of language, I do recommend that you become fluent in Prolog before you read this book.
I used to recommend Jurafsky and Martin for people starting out learning language processing, but now I think I can recommend this book for not only the big picture but the details of this interesting field as well.An Introduction to Language Processing with Perl and Prolog: An Outline of Theories, Implementation, and Application with Special Consideration of English, French, and German (Cognitive Technologies) OverviewThis book teaches the principles of natural language processing and covers linguistics issues. It also details the language-processing functions involved, including part-of-speech tagging using rules and stochastic techniques. A key feature of the book is the author's hands-on approach throughout, with extensive exercises, sample code in Prolog and Perl, and a detailed introduction to Prolog. The book is suitable for researchers and students of natural language processing and computational linguistics.

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From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) Review

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From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) ReviewIt is easy to read with lots of examples. Gives planty of ideas if you are interested in corpus study.From Corpus to Classroom: Language Use and Language Teaching (Cambridge Language Teaching Library) OverviewFrom Corpus to Classroom summarises and makes accessible recent work in corpus research, focusing particularly on spoken data. It is based on analysis of corpora such as CANCODE and Cambridge International Corpus, and written with particular reference to the development of corpus-informed pedagogy.The book explains how corpora can be designed and used, and focuses on what they tell us about language teaching. It examines the relevance of corpora to materials writers, course designers and language teachers and considers the needs of the learner in relation to authentic data. It shows how the answers to key questions such as 'Is there a basic, everyday vocabulary for English?', 'How should idioms be taught?' and 'What are the most common spoken language chunks?' are best explored by means of a clearer understanding of the workings of language in context.

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Foundations of Statistical Natural Language Processing Review

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Foundations of Statistical Natural Language Processing ReviewThis is the best book I've ever read on computational linguistics. It should be ideal for both linguists who want to learn about statistical language processing and those building language applications who want to learn about linguistics. This book isn't even published and it's now my most highly used reference book, joining gems such as Cormen, Leiserson and Rivest's algorithm book, Quirk et al.'s English Grammar, and Andrew Gelman's Bayesian statistics book (three excellent companions to this book, by the way).
The book is written more like a computer science or math book in that it starts absolutely from scratch, but moves quickly and assumes a sophisticated reader. The first one hundred or so pages provide background in probability, information theory and linguistics.
This book covers (almost) every current trend in NLP from a statistical perspective: syntactic tagging, sense disambiguation, parsing, information retrieval, lexical subcategorization, Hidden Markov Models, and probabilistic context-free grammars. It also covers machine translation and information retrieval in later chapters.
It covers all the statistical techniques used in NLP from Bayes' law through to maximum entropy modeling, clustering: nearest neighbors and decision trees, and much more.
What you won't find is information on applications to higher-level discourse and dialogue phenomena like pronoun resolution or speech act classification.Foundations of Statistical Natural Language Processing OverviewStatistical approaches to processing natural language text have becomedominant in recent years. This foundational text is the first comprehensiveintroduction to statistical natural language processing (NLP) to appear. The bookcontains all the theory and algorithms needed for building NLP tools. It providesbroad but rigorous coverage of mathematical and linguistic foundations, as well asdetailed discussion of statistical methods, allowing students and researchers toconstruct their own implementations. The book covers collocation finding, word sensedisambiguation, probabilistic parsing, information retrieval, and otherapplications.

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