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Natural Language Processing for PROLOG Programmers [Facsimile] [Paperback]

Michael A. Covington
5.0 out of 5 stars  See all reviews (1 customer review)
Price: £41.99 & this item Delivered FREE in the UK with Super Saver Delivery. See details and conditions
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Book Description

3 Aug 1993 0136292135 978-0136292135 1

An examination of natural language processing in Prolog for those who know Prolog but not linguistics.


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Product details

  • Paperback: 348 pages
  • Publisher: Prentice Hall; 1 edition (3 Aug 1993)
  • Language: English
  • ISBN-10: 0136292135
  • ISBN-13: 978-0136292135
  • Product Dimensions: 18 x 1.8 x 24.2 cm
  • Average Customer Review: 5.0 out of 5 stars  See all reviews (1 customer review)
  • Amazon Bestsellers Rank: 1,475,694 in Books (See Top 100 in Books)
  • See Complete Table of Contents

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Most Helpful Customer Reviews
7 of 7 people found the following review helpful
5.0 out of 5 stars an excellent book for NLP beginners 30 Jan 2001
By A Customer
Format:Paperback
We used this book as a textbook in an introductory NLP course. The author is aware of a beginner student and his/her capabilities. Obviously, he has written this book with empathy, always putting himself in place of the reader and writing having understood the student. The characteristics are as follows: (1) The organization is well designed. Topics are ordered well and no prerequisite is ahead of the chapter you are reading. (2) The topics are cut in manageable pieces. Every chapter has been divided in sections and every section in subsections. A subsection is at most 3 pages. This arrangement motivated me a lot and made me think that I made a progress every time I finished a section or subsection. (3) The narrative text is very explanatory, friendly, simple and pure. There is no unnecessary detail. Everything you read contributes to your understanding of the subject matter. (4) The book is full of figures, examples, and illustrations. This gives you chance to follow some explanations of the author concretely and some are put there in case you want to master some details, often on a complete working implementation. (5) There are abundant exercises. I recommend you not to skip them. You may skip some according to your needs and according your focus on the subject. I think there is more than you need. I suppose it's so to cover all people's expectations. The exercises are usually small in size so that you can quickly master the subject in a piecemeal fashion. Unfortunately, there are no solutions of them on the back, but I almost never needed them because they are simple, relevant to the subject just explained, and usually an extension or a slight derivation of what had been explained, which helps you consolidate the subject. Moreover, the product of your effort is not irrelevant to NLP, you wouldn't differentiate it if the author explained it along with the main topic. Some exercises are prerequisite for others. This is a better approach than to give one big exercise that is tedious, and thus blocking! Instead the author cut it in pieces and spread it over a section, which makes your task manageable, and as a result motivates you. Overall, NLP is such a subject that there are many small details and it's hard to organize them. I think the author has done a marvellous job as he managed to organize them considerably, helping the reader have a good representation of NLP in the end, when the book is over.
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Amazon.com: 4.0 out of 5 stars  1 review
9 of 9 people found the following review helpful
4.0 out of 5 stars One of the best intros to NLP 20 Oct 2004
By Randall Helzerman - Published on Amazon.com
Format:Paperback
This book, while a bit dated, is still the best discussion of a wide variety of (nonstatistical) parsing methods in print. The best part of this book is the table on p. 191, where the author compares the runtimes of the various parsing algorithms, and shows that the one with the scariest "big O" complexity is actually the fastest in practice. This is a very important lesson to impart to budding computer scientists: use "big O" analysis wisely, not as the voice of God, as to which algorithm is the best practical algorithm.

I've given this book 4 stars instead of 5 stars because it is a little out of date: look elsewhere for the best discussions of quantifier scope handling and discourse. Also, it doesn't contain any discussions on the what is today the more fashionable statistical-based approaches, but the jury is still out, IMHO, on whether this is a drawback or not.
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