000			03429nam a22005415i 4500
001			978-3-319-21311-8
003			DE-He213
005			20200421111701.0
007			cr nn 008mamaa
008			160712s2016 gw | s |||| 0|eng d
020			_a9783319213118 _9978-3-319-21311-8
024	7		_a10.1007/978-3-319-21311-8 _2doi
050		4	_aP98-98.5
072		7	_aUYQL _2bicssc
072		7	_aCOM042000 _2bisacsh
082	0	4	_a006.35 _223
245	1	0	_aHybrid Approaches to Machine Translation _h[electronic resource] / _cedited by Marta R. Costa-juss�a, Reinhard Rapp, Patrik Lambert, Kurt Eberle, Rafael E. Banchs, Bogdan Babych.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016.
300			_aIX, 205 p. 45 illus., 18 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aTheory and Applications of Natural Language Processing, _x2192-032X
505	0		_aPreface -- Foreword -- Chapter 1. Hybrid Machine Translation Overview -- Part 1: Adding Linguistics into SMT -- Chapter 2. Controllent Ascent: Imbuing Statistical MT with Linguistic knowledge -- Chapter 3. Hybrid Word Alignment -- Chapter 4. Syntax in SMT -- Part 2. Using Machine Learning in MT -- Chapter 5. Machine Learning in RBMT -- Chapter 6. Language-Independent Hybrid MT -- Part 3. Hybrid NLP tools useful for MT -- Chapter 7. Use of Dependency Parsers in MT -- Chapter 8. Word Sense Disambiguation in MT. .
520			_aThis volume provides an overview of the field of Hybrid Machine Translation (MT) and presents some of the latest research conducted by linguists and practitioners from different multidisciplinary areas. Nowadays, most important developments in MT are achieved by combining data-driven and rule-based techniques. These combinations typically involve hybridization of different traditional paradigms, such as the introduction of linguistic knowledge into statistical approaches to MT, the incorporation of data-driven components into rule-based approaches, or statistical and rule-based pre- and post-processing for both types of MT architectures. The book is of interest primarily to MT specialists, but also - in the wider fields of Computational Linguistics, Machine Learning and Data Mining - to translators and managers of translation companies and departments who are interested in recent developments concerning automated translation tools.
650		0	_aComputer science.
650		0	_aComputational linguistics.
650		0	_aTranslation and interpretation.
650	1	4	_aComputer Science.
650	2	4	_aLanguage Translation and Linguistics.
650	2	4	_aComputational Linguistics.
650	2	4	_aTranslation.
700	1		_aCosta-juss�a, Marta R. _eeditor.
700	1		_aRapp, Reinhard. _eeditor.
700	1		_aLambert, Patrik. _eeditor.
700	1		_aEberle, Kurt. _eeditor.
700	1		_aBanchs, Rafael E. _eeditor.
700	1		_aBabych, Bogdan. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319213101
830		0	_aTheory and Applications of Natural Language Processing, _x2192-032X
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-21311-8
912			_aZDB-2-SCS
942			_cEBK
999			_c54970 _d54970