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Christopher D. Manning
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Введение в информационный поиск

4.18  ·  Rating details ·  345 ratings  ·  23 reviews
Введение в информационный поиск - это первый учебник, в котором наряду с классическим поиском рассматриваются веб-поиск, а также классификация и кластеризация текстов. Учебник написан с точки зрения информатики и содержит современное изложение всех аспектов проектирования и реализации систем сбора, индексирования и поиска документов, методов оценки таких систем, а также ...more
528 pages
Published 2014 by Вильямс (first published July 7th 2008)
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Average rating 4.18  · 
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Emily
Sep 12, 2011 rated it really liked it  ·  review of another edition
Shelves: 2011
This is a very useful book, available for free online. It introduces the major concepts of IR in a clear way. At the end of most chapters is an optional discussion of advanced topics. I wish the book went a little bit into examples of programming, but that's for someone else to take on, I guess.

Librarians should be forced to read at least parts of this book in school, to better understand why Google is eating our lunch.
Alex Ott
Great book that provides introduction information retrieval + related topics, such as, elements of machine learning, etc. There is good balance between theoretical foundations & simplicity, but it doesn't as simple as other "popular" books, such as "Programming Collective Intelligence", etc.
You can also find "pre-release" version of the book in the form of PDF (see links in other reviews)...
Terran M
May 19, 2018 rated it it was amazing  ·  review of another edition
This is an excellent theoretical and foundational book on information retreival (AKA document search), and also covers some document classification. It does not cover any specific software packages or tools. You don't need this book to throw a bunch of documents into elasticsearch, but you do need it to understand why you're not getting the results you want back and how to fix it.

Note that this book was written in 2008 and it advocates support vector machines for classification; modern practice
...more
Jaslyn
Here's a pretty useful online summary: https://nlp.stanford.edu/IR-book/html...
Benoit Blanchon
Mar 29, 2018 rated it really liked it  ·  review of another edition
Shelves: algorithm
As it's the only book on IR, it's both the best and the worst at the same time.
The content is very interesting but the writing style is horrible.
Nick Black
Feb 16, 2009 marked it as to-read  ·  review of another edition
Recommended to Nick by: Greg Linden
A strong review from http://glinden.blogspot.com/2009/02/b... plus the free pdf (hurrah!) at http://www-csli.stanford.edu/~hinrich... equals an excited dankling, especially as I'm pretty weak overall on search (we leave the n-dimensional SVM's -- and machine learning in general -- to our scientists, whereas my job is to take their data and operator schemata and set it to soaking every last cycle and flooding every last bus), but it's becoming pretty much a canonical element of the "advanced ...more
Maria Mateva
Feb 24, 2013 rated it it was amazing  ·  review of another edition
Shelves: science, favorites
Christopher Manning is a rock star in both the NLP and information retrieval fields.

I used this book as a guide and source for the course in IR in Sofia University.
It is well-written, gradual and observes most aspects of IR, with some machine learning, computational linguistics and algorithmic flavours. I recommend it to anyone interested in the field.
Joe Malicki
Jun 01, 2009 rated it it was amazing  ·  review of another edition
Shelves: cs
Amazing book. An information retrieval book written by computer scientists/engineers rather than librarians. That shouldn't be something worth getting excited about, but unfortunately all of the other good people apparently just went to work for Google instead of writing good textbooks.
Ji
Jan 28, 2017 rated it liked it  ·  review of another edition
Scanned through quickly. A bit disappointed since I don't feel like learning much new things. Most important concepts are either in the lines of Data management or data mining techniques. Maybe I've missed the main part.
Anosh
Jul 09, 2012 rated it really liked it  ·  review of another edition
I liked the book overall. I loved the algorithmic optimizations and the attention paid to hardware during algorithm design. I didn't like the number of tricks/hacks in the "intelligence" part in the field of information retrieval.
Ryfm
Oct 13, 2010 rated it really liked it  ·  review of another edition
good introduction for everyone who plans to work in IR field
Valia
Feb 11, 2012 rated it it was amazing  ·  review of another edition
Shelves: ai, ebook
Хорошая книга. Непонятно только, куда это теперь все применять.
Duc
Jan 30, 2019 rated it really liked it  ·  review of another edition
Informative, but a bit dry.
Anand
Mar 25, 2013 rated it really liked it  ·  review of another edition
Excellent read on IR for beginners'
Alon Gutman
Sep 19, 2012 rated it really liked it  ·  review of another edition
Very good introduction to search and also good contains good introduction to Machine learning.
Frank
Jul 23, 2012 rated it it was amazing  ·  review of another edition
This is more like a practical hands-on on all aspects of modern IR. Fantastic read!
Richa Sharma
awesome book,explains the detail of how information is being retrieved is great. Very helpful and explanatory content.
Rene Treffer
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Apr 20, 2019
Kairiki
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Mar 03, 2012
Dan Creswell
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Philip Scuderi
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Moozie Jimmy Brown
rated it it was ok
Apr 03, 2018
Martin
rated it it was amazing
Aug 27, 2018
Ehnaton
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Eugene
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Barrett Ames
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Nicolas
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Sam
Aug 18, 2010 rated it really liked it  ·  review of another edition
Good broad-based overview of information retrieval concepts
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Professor of Linguistics and Computer Science, Natural Language Processing Group, Stanford University
“A human is not a device that reliably reports a gold standard judgment of relevance of a document to a query.” 1 likes
“In principle, more analytic power can be achieved by varying multiple things at once in an uncorrelated (random) way, and doing standard analysis, such as multiple linear regression. In practice, though, A/B testing is widely used, because A/B tests are easy to deploy, easy to understand, and easy to explain to management.” 1 likes
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