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Marie-Francine Moens - Information Extraction: Algorithms and Prospects in a Retrieval Context - 9789048172467 - V9789048172467
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Information Extraction: Algorithms and Prospects in a Retrieval Context

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Description for Information Extraction: Algorithms and Prospects in a Retrieval Context Paperback. Series: The Information Retrieval Series. Num Pages: 260 pages, biography. BIC Classification: KNTX; UND; UYQL; UYQP. Category: (P) Professional & Vocational. Dimension: 240 x 160 x 15. Weight in Grams: 419.

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 ... Read more

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Product Details

Format
Paperback
Publication date
2010
Publisher
Springer Netherlands
Number of pages
260
Condition
New
Series
The Information Retrieval Series
Number of Pages
246
Place of Publication
Dordrecht, Netherlands
ISBN
9789048172467
SKU
V9789048172467
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

Reviews for Information Extraction: Algorithms and Prospects in a Retrieval Context
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 ... Read more

Goodreads reviews for Information Extraction: Algorithms and Prospects in a Retrieval Context


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