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130626 ||| eng |
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|a 9781402049934
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|a Moens, Marie-Francine
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|a Information Extraction: Algorithms and Prospects in a Retrieval Context
|h Elektronische Ressource
|c by Marie-Francine Moens
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| 250 |
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|a 1st ed. 2006
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| 260 |
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|a Dordrecht
|b Springer Netherlands
|c 2006, 2006
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| 300 |
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|a XIV, 246 p
|b online resource
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|a Information Extraction and Information Technology -- Information Extraction from an Historical Perspective -- The Symbolic Techniques -- Pattern Recognition -- Supervised Classification -- Unsupervised Classification Aids -- Integration of Information Extraction in Retrieval Models -- Evaluation of Information Extraction Technologies -- Case Studies -- The Future of Information Extraction in a Retrieval Context
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|a The Computer Industry
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|a Library science
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|a Computer industry
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|a Artificial Intelligence
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|a Information Storage and Retrieval
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| 653 |
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|a Information storage and retrieval systems
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| 653 |
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|a Artificial intelligence
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|a Library Science
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| 653 |
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|a Natural Language Processing (NLP)
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| 653 |
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|a Automated Pattern Recognition
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| 653 |
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|a Natural language processing (Computer science)
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| 653 |
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|a Pattern recognition systems
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|a eng
|2 ISO 639-2
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| 989 |
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|b Springer
|a Springer eBooks 2005-
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| 490 |
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|a The Information Retrieval Series
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| 028 |
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|a 10.1007/978-1-4020-4993-4
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|u https://doi.org/10.1007/978-1-4020-4993-4?nosfx=y
|x Verlag
|3 Volltext
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|a 020
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|a 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
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