Programming Books C Java PHP Python Learn more Browse Programming Books

Sorry, this item is not available in
Image not available for
Color:
Image not available

To view this video download Flash Player

 


or
Sign in to turn on 1-Click ordering
Sell Us Your Item
For a $3.05 Gift Card
Trade in
More Buying Choices
Have one to sell? Sell yours here
Tell the Publisher!
I'd like to read this book on Kindle

Don't have a Kindle? Get your Kindle here, or download a FREE Kindle Reading App.

Information Extraction: Algorithms and Prospects in a Retrieval Context (The Information Retrieval Series) [Hardcover]

Marie-Francine Moens

List Price: $179.00
Price: $143.20 & FREE Shipping. Details
You Save: $35.80 (20%)
o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o o
Only 2 left in stock (more on the way).
Ships from and sold by Amazon.com. Gift-wrap available.
Want it Tuesday, July 15? Choose One-Day Shipping at checkout. Details
Free Two-Day Shipping for College Students with Amazon Student

Formats

Amazon Price New from Used from
Hardcover $143.20  
Paperback $162.91  
Shop the new tech.book(store)
New! Introducing the tech.book(store), a hub for Software Developers and Architects, Networking Administrators, TPMs, and other technology professionals to find highly-rated and highly-relevant career resources. Shop books on programming and big data, or read this week's blog posts by authors and thought-leaders in the tech industry. > Shop now

Book Description

July 28, 2006 1402049870 978-1402049873 2006

This book covers content recognition in text, elaborating on past and current most successful algorithms and their application in a variety of settings: news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text. Today, there is considerable interest in integrating the results of information extraction in retrieval systems, because of the demand for search engines that return precise answers to flexible information queries.



Editorial Reviews

Review

From the reviews:

"Information Extraction (IE) and Information Retrieval (IR) are core enabling technologies … . In this text, Moens brings these two techniques together to illustrate how information derived using IE could be highly beneficial in IR systems. … the text is highly readable and aimed at both practitioners and researchers … . One trait that I offer particular praise to the author for is the pragmatic presentation of ideas. … the text should be beneficial both to seasoned professionals in this area and relative newcomers." (Tom Betts, Informer, Winter 2006/2007)

"After definition and explanation of the basic concepts and description of the historical development of the area, the past and current most successful algorithms and their application in a variety of domains are discussed. Especially important is the explanation of statistical and machine learning algorithms for information detection and classification and integration of their results in probabilistic retrieval models. … Because its broad coverage and clear and sound explanation it is suitable and valuable both for researchers and for students." (Antonín Ríha, Zentralblatt MATH, Vol. 1108 (10), 2007)

"This book … provide a comprehensive overview of text-extraction algorithms. It does well in … explaining the intricacies of the basic approaches and concepts used. … for advanced undergraduate students, graduate students, researchers, and people working in the field, the book is a good starting point for learning the basics. … I would recommend the book for those who need to get into … the field. … the book is one that should be on your must-read list if you are involved in this field." (Karthik Gajjala, ACM Computing Reviews, Vol. 49 (2), February, 2008)

From the Back Cover

Information extraction regards the processes of structuring and combining content that is explicitly stated or implied in one or multiple unstructured information sources. It involves a semantic classification and linking of certain pieces of information and is considered as a light form of content understanding by the machine. Currently, there is a considerable interest in integrating the results of information extraction in retrieval systems, because of the growing demand for search engines that return precise answers to flexible information queries. Advanced retrieval models satisfy that need and they rely on tools that automatically build a probabilistic model of the content of a (multi-media) document.

The book focuses on content recognition in text. It elaborates on the past and current most successful algorithms and their application in a variety of domains (e.g., news filtering, mining of biomedical text, intelligence gathering, competitive intelligence, legal information searching, and processing of informal text). An important part discusses current statistical and machine learning algorithms for information detection and classification and integrates their results in probabilistic retrieval models. The book also reveals a number of ideas towards an advanced understanding and synthesis of textual content.

The book is aimed at researchers and software developers interested in information extraction and retrieval, but the many illustrations and real world examples make it also suitable as a handbook for students.


Product Details


More About the Author

Discover books, learn about writers, read author blogs, and more.

Customer Reviews

There are no customer reviews yet.
5 star
4 star
3 star
2 star
1 star
Share your thoughts with other customers

Sell a Digital Version of This Book in the Kindle Store

If you are a publisher or author and hold the digital rights to a book, you can sell a digital version of it in our Kindle Store. Learn more

Forums

There are no discussions about this product yet.
Be the first to discuss this product with the community.
Start a new discussion
Topic:
First post:
Prompts for sign-in
 



Look for Similar Items by Category