000			03672nam a22004935i 4500
001			978-1-4020-4993-4
003			DE-He213
005			20251006084517.0
007			cr nn 008mamaa
008			100301s2006 ne | s |||| 0|eng d
020			_a9781402049934
020			_a99781402049934
024	7		_a10.1007/978-1-4020-4993-4 _2doi
082	0	4	_a025.04 _223
100	1		_aMoens, Marie-Francine. _eauthor.
245	1	0	_aInformation Extraction: Algorithms and Prospects in a Retrieval Context _h[electronic resource] / _cby Marie-Francine Moens.
264		1	_aDordrecht : _bSpringer Netherlands, _c2006.
300			_aXIII, 246 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aInformation 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.
520			_aInformation 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.
650		0	_aCOMPUTER SCIENCE.
650		0	_aINFORMATION STORAGE AND RETRIEVAL SYSTEMS.
650		0	_aARTIFICIAL INTELLIGENCE.
650		0	_aTRANSLATORS (COMPUTER PROGRAMS).
650		0	_aOPTICAL PATTERN RECOGNITION.
650		0	_aCOMPUTER INDUSTRY.
650	1	4	_aCOMPUTER SCIENCE.
650	2	4	_aINFORMATION STORAGE AND RETRIEVAL.
650	2	4	_aARTIFICIAL INTELLIGENCE (INCL. ROBOTICS).
650	2	4	_aLANGUAGE TRANSLATION AND LINGUISTICS.
650	2	4	_aPATTERN RECOGNITION.
650	2	4	_aTHE COMPUTER INDUSTRY.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9781402049873
856	4	0	_uhttp://dx.doi.org/10.1007/978-1-4020-4993-4 _zVer el texto completo en las instalaciones del CICY
912			_aZDB-2-SCS
942			_2ddc _cER
999			_c61087 _d61087