000 03315nam a22004215i 4500
001 978-3-642-22743-1
003 DE-He213
005 20140220083259.0
007 cr nn 008mamaa
008 111102s2012 gw | s |||| 0|eng d
020 _a9783642227431
_9978-3-642-22743-1
024 7 _a10.1007/978-3-642-22743-1
_2doi
050 4 _aQA75.5-76.95
072 7 _aUY
_2bicssc
072 7 _aCOM014000
_2bisacsh
082 0 4 _a004
_223
100 1 _aPetrov, Slav.
_eauthor.
245 1 0 _aCoarse-to-Fine Natural Language Processing
_h[electronic resource] /
_cby Slav Petrov.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2012.
300 _aXXII, 106 p.
_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 _a1.Introduction -- 2.Latent Variable Grammars for Natural Language Parsing -- 3.Discriminative Latent Variable Grammars -- 4.Structured Acoustic Models for Speech Recognition -- 5.Coarse-to-Fine Machine Translation Decoding -- 6.Conclusions and Future Work -- Bibliography.
520 _aThe impact of computer systems that can understand natural language will be tremendous. To develop this capability we need to be able to automatically and efficiently analyze large amounts of text. Manually devised rules are not sufficient to provide coverage to handle the complex structure of natural language, necessitating systems that can automatically learn from examples. To handle the flexibility of natural language, it has become standard practice to use statistical models, which assign probabilities for example to the different meanings of a word or the plausibility of grammatical constructions. This book develops a general coarse-to-fine framework for learning and inference in large statistical models for natural language processing. Coarse-to-fine approaches exploit a sequence of models which introduce complexity gradually. At the top of the sequence is a trivial model in which learning and inference are both cheap. Each subsequent model refines the previous one, until a final, full-complexity model is reached. Applications of this framework to syntactic parsing, speech recognition and machine translation are presented, demonstrating the effectiveness of the approach in terms of accuracy and speed. This book is intended for students and researchers interested in statistical approaches to Natural Language Processing.  Slav’s work Coarse-to-Fine Natural Language Processing represents a major advance in the area of syntactic parsing, and a great advertisement for the superiority of the machine-learning approach. Eugene Charniak (Brown University)
650 0 _aComputer science.
650 1 4 _aComputer Science.
650 2 4 _aComputer Science, general.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642227424
830 0 _aTheory and Applications of Natural Language Processing,
_x2192-032X
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-22743-1
912 _aZDB-2-SHU
999 _c102078
_d102078