000			02856nam a22003618i 4500
001			CR9780511976247
003			UkCbUP
005			20200124160256.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			101011s2011||||enk o ||1 0|eng|d
020			_a9780511976247 (ebook)
020			_z9780521896139 (hardback)
040			_aUkCbUP _beng _erda _cUkCbUP
050	0	0	_aQA76.9.N38 _bM53 2011
082	0	0	_a005.4/37 _222
100	1		_aMihalcea, Rada, _d1974- _eauthor.
245	1	0	_aGraph-based natural language processing and information retrieval / _cRada Mihalcea, Dragomir Radev.
246	3		_aGraph-based Natural Language Processing & Information Retrieval
264		1	_aCambridge : _bCambridge University Press, _c2011.
300			_a1 online resource (viii, 192 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
500			_aTitle from publisher's bibliographic system (viewed on 05 Oct 2015).
505	8		_aMachine generated contents note: Part I. Introduction to Graph Theory: 1. Notations, properties, and representations; 2. Graph-based algorithms; Part II. Networks: 3. Random networks; 4. Language networks; Part III. Graph-Based Information Retrieval: 5. Link analysis for the world wide web; 6. Text clustering; Part IV. Graph-Based Natural Language Processing: 7. Semantics; 8. Syntax; 9. Applications.
520			_aGraph theory and the fields of natural language processing and information retrieval are well-studied disciplines. Traditionally, these areas have been perceived as distinct, with different algorithms, different applications and different potential end-users. However, recent research has shown that these disciplines are intimately connected, with a large variety of natural language processing and information retrieval applications finding efficient solutions within graph-theoretical frameworks. This book extensively covers the use of graph-based algorithms for natural language processing and information retrieval. It brings together topics as diverse as lexical semantics, text summarization, text mining, ontology construction, text classification and information retrieval, which are connected by the common underlying theme of the use of graph-theoretical methods for text and information processing tasks. Readers will come away with a firm understanding of the major methods and applications in natural language processing and information retrieval that rely on graph-based representations and algorithms.
650		0	_aNatural language processing (Computer science)
650		0	_aGraphical user interfaces (Computer systems)
700	1		_aRadev, Dragomir, _d1968- _eauthor.
776	0	8	_iPrint version: _z9780521896139
856	4	0	_uhttps://doi.org/10.1017/CBO9780511976247
999			_c519919 _d519917