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Graph-based Natural Language Processing and Information Retrieval 1st Edition

3.3 out of 5 stars 3 customer reviews
ISBN-13: 978-0521896139
ISBN-10: 0521896134
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Editorial Reviews

Review

"For the first time, a computational framework that unifies many algorithms and representations from the fields of natural language processing and information retrieval. This book is a comprehensive introduction to both theory and practice."
Giorgio Satta, University of Padua

Book Description

This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. Readers will come away with a firm understanding of the major methods and applications of these topics that rely on graph-based representations and algorithms.
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Product Details

  • Hardcover: 202 pages
  • Publisher: Cambridge University Press; 1 edition (April 11, 2011)
  • Language: English
  • ISBN-10: 0521896134
  • ISBN-13: 978-0521896139
  • Product Dimensions: 6.1 x 0.5 x 9.2 inches
  • Shipping Weight: 15.5 ounces (View shipping rates and policies)
  • Average Customer Review: 3.3 out of 5 stars  See all reviews (3 customer reviews)
  • Amazon Best Sellers Rank: #1,226,280 in Books (See Top 100 in Books)

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Top Customer Reviews

Format: Hardcover Verified Purchase
I have bought this book right when I stepped into Text mining (NLP) area for my current job as Data Scientist. For many months the book accrued dust on my shelf, it was until recently that I read almost half of the book in two sittings. Here is why I suddenly found this book very useful:

I was cooking up some ideas on using a graph based approach to model the problem. I spent many hours in thinking on this problem. Eventually I realized, I need a well articulated body of knowledge to refine my thinking, give words to ideas, reference other people's similar work, and avoid reinventing the wheel. This book did all of the above, and more that I am omitting because of space, and that not yet realized.
There is a subtle point here, I will spell out for your convenience, this is for you if you are ready. In addition, it does not go in excess detail that you lose track of your thinking, at the same time it refers enough examples from literature on every topic that you can pursue further to find more details; therefore it's a strong reference book and may not be a textbook.

My rating is 4 out of 5:
1) I do not see this as a textbook.
2) The scope is very specialized.
3) You have to have a minimum level of exposure to graph algorithms, linear algebra, and probability to really benefit from the book.
4) A self-contained appendix to refresh above mentioned topics may improve acceptance of this book, and hence the rating.

I hope this helps the future reader.
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Format: Kindle Edition Verified Purchase
This book covers lots of topics (as you see can from its TOC) but does not provide sound explanation, intuition, or theory. I would have given a one star rating but my two star rating reflects the fact that you get a list of topics at one place that you can use to further explore.
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Format: Hardcover Verified Purchase
While this book provides a good background on NLP processing wherein the linguistic entities are individually represented by nodes (and/or edges) in a graph, the title misled me a bit since there is no discussion of theoretical approaches where each linguistic entity is represented by a directed graph (i.e. typed feature structures, Carpenter 1992, etc.) and the operations (i.e. graph unification) are defined over these complex structures. This being my area of interest--and what I was looking for when purchasing the book--I thought I'd mention that this book does not cover the topic.
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