Overview

Information extraction (IE) is a new technology enabling relevant content to be extracted from textual information available electronically. IE essentially builds on natural language processing and computational linguistics, but it is also closely related to the well established area of information retrieval and involves learning. In concert with other promising and emerging information engineering technologies like data mining, intelligent data analysis, and text summarization, IE will play a crucial role for scientists and professionals as well as other end-users who have to deal with vast amounts of information, for example from the Internet. As the first book solely devoted to IE, it is of relevance to anybody interested in new and emerging trends in information processing technology.

Full Product Details

Author:   Maria T. Pazienza
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Edition:   1997 ed.
Volume:   1299
Dimensions:   Width: 21.60cm , Height: 1.20cm , Length: 27.90cm
Weight:   0.730kg
ISBN:  

9783540634386

ISBN 10:   354063438
Pages:   222
Publication Date:   13 August 1997
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Information extraction as a core language technology.- Information extraction: Techniques and challenges.- Concepticons vs. lexicons: An architecture for multilingual information extraction.- Lexical acquisition and information extraction.- Technical terminology for domain specification and content characterisation.- Short query linguistic expansion techniques: Palliating one-word queries by providing intermediate structure to text.- Information retrieval: Still butting heads with natural language processing?.- Semantic matching: Formal ontological distinctions for information organization, extraction, and integration.- Machine learning for information extraction.- Modeling and querying semi-structured data.

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