Customer Reviews

Introduction to Information Retrieval

5 star:		(9)
4 star:		(3)
3 star:		(1)
2 star:		(0)
1 star:		(1)

 

 

I am a big fan of the authors 1999 book on Statistical Natural Language Processing, and I and was thrilled when I found this new book online -- just search for "Information Retrieval" on Google.

In these two books, they describe the theory behind a vast toolbox which can be used to construct new tools/products for the Internet. Now I can go back to them when the need arises.

For starters, I appreciate the detailed theoretical explanations of topics that I could not find in other texts, and the references to related work are especially helpful. One of the other books I read was Information Retrieval by Grossman, which is an older book but has a more condensed style compared to this. Grossman's discussion of clustering was more high level and referenced a few more papers that I found useful. That helped increase my interest to read through these chapters in which offer greater detail.

Before I felt like I could place each topic in its appropriate context, I had to spend six months of reading both the books, playing with code and finding s/w packages, searching the research literature, reading papers and other books, and then cycling back to the books. Here's are some suggestions for things I'd like to see:

1. A set of recomended programming tools: in some books on Perl -- such as the chapter "Natural Language Tools" in pages 149-171 in "Advanced Perl Programming" by Simon Cozens (O'Reilly) -- you get a very "quick & dirty" introduction to maybe 20-30% of the concepts in these two books along with ways to implement and play around with them. Although Perl has many natural language processing tools, the Cozens book cuts to the chase, explains which are the best tools, and shows you how to use them. I think knowing such shortcuts aids in learning how to apply and improve on them. The more complex and sophisticated topics, the more likely to make it out into the real world if they are easy to play with.

2. More data/examples on what does/doesn't work with end-users: Numbers, graphs, and charts are all good stuff. I always appreciate it when the authors referenced quantitative comparisons, real-world products, and history of Internet. One of the reasons I had to consult the research literature was to broaden my understanding of quantitative comparisons between different techniques involving end-users, which were typically done in the context of complete systems studies that users could try out.

Thanks,
-Sri

Managing Gigabytes used to be my favorite book on search, but it is getting quite dated as this point. This new book is by three search gurus, Chris Manning, Prabhakar Raghavan (head of Yahoo Research), and Hinrich Schutze, and the depth of their expertise shows.

This book not only describes how to build a search engine (including crawling, indexing, ranking, classification, and clustering), but also has many of the insights you can only get from lengthy experience using these techniques at large scale.

Definitely my new favorite book on search. If you work in search or just have an interest in the field, it is a great read.

Although i'm a newbie in information retrieval field (I'm more of a machine learning, computer vision, timeseries person),
I like the book most for the following two reasons :
(1) detailed explanation into the level of implementation in many cases (data structures//memory size etc..)
(2) good review on practice vs. theory. The authors present diverse attractive theories, and on the other hand, discusses why sometimes just simpler methods are hard to be beaten down by those more complicated methods from their experience in practice.

I like that!

This is my first book about information retrieval, and I think that is perfect! The book cover all the modern topics in the information retrieval field. It's very clear and really simple to understand.
Great book!
Congratulations to the authors!

I have no desire to build an internet search engine, so I'm not the target audience. However, I do work with large corpora, some of which are unindexed. When one search I programmed (in R) took 14 hours to complete (this after one attempt produced unusable results due to a bug and another crashed twelve hours in due to the power saver mode kicking in), I knew I had to find a better way.

I knew from the free sample that this book was what I was looking for. Thinking this would be a completely a new field to me, I was surprised how much I already knew. Some of it is not relevant to corpus linguists (result ranking for example), but if you're a corpus linguist and want to build an index for your corpus, I doubt you'll find a better book than this.

And the Kindle edition is done well, which is not always the case. Websites are hyperlinked and you can jump to the next or previous section with the 5-way controller.

This is an excellent book, which very rapidly takes the reader to the heart of this very important field. While its title is "Introduction to.." is is definitely NOT a book for beginners. It starts with the assumption that the reader has the knowledge level of a recent CS graduate and proceeds to build on it rapidly. In the key chapters dealing with classification, the vector space model, etc. it is highly mathematical.

A must-have resource book for graduate students and anyone working seriously in the IR and text-mining fields. The references alone make it worth buying.

My only gripes are:
- the layout (LaTeX driven?) which has placed all the figures and tables at the tops of pages;
- the occasional lapse in proof-reading, e.g. "becuase". CUP could perhaps have taken a little more care. The book is worth it.

3.5 stars if half stars were allowed.

I used this book to teach an information retrieval course at CSU Long Beach for seniors and graduate students.
For the most part I thought the book succeeded in providing a satisfactory overview of
the issues behind designing and implementing a modern IR system. I felt however that the book needed more examples and
structure. Too often the examples that were presented were done so in a colloquial fashion that often made them hard to follow.

I would have also preferred more depth in each chapter. With a total of 21 chapters, and only 400+ pages,
and several pages in each chapter dedicated to exercises and references, it seemed that too often the authors would abruptly
move on to the next topic, without fully developing the current one. This pattern became most noticeable beginning with
Chapter 4 on Index Construction, and continuing on through the more advanced chapters.

Finally, I do recommend this book to those with a solid background in computer science. But be prepared for some disappointment
for the reasons mentioned above.

if you want to have a basic understanding about search engine, this book will be the best choice. however, if you hope to do research in this area, this book is not enough.

A very detailed and implementation focused book. This book contains enormous amount of wisdom that could only have been garnered by actually implementing IR in the real-world.
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Gratuitous use of Greek symbolism coupled with a hit and miss approach with examples (sometimes they are very good and sometimes they just don't bother) mean that I found myself scurrying to the web to look for alternative explanations of things that this book was supposed to introduce me to. Only half does the job.