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003 DE-He213
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007 cr nn 008mamaa
008 101013s2010 ne | s |||| 0|eng d
020 _a9789048193523
_9978-90-481-9352-3
024 7 _a10.1007/978-90-481-9352-3
_2doi
050 4 _aP98-98.5
072 7 _aCFX
_2bicssc
072 7 _aLAN009000
_2bisacsh
072 7 _aCOM018000
_2bisacsh
082 0 4 _a410.285
_223
100 1 _aBunt, Harry.
_eeditor.
245 1 0 _aTrends in Parsing Technology
_h[electronic resource] :
_bDependency Parsing, Domain Adaptation, and Deep Parsing /
_cedited by Harry Bunt, Paola Merlo, Joakim Nivre.
264 1 _aDordrecht :
_bSpringer Netherlands :
_bImprint: Springer,
_c2010.
300 _aX, 298 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aText, Speech and Language Technology,
_x1386-291X ;
_v43
505 0 _aCurrent Trends in Parsing Technology -- Single Malt or Blended? A Study in Multilingual Parser Optimization -- A Latent Variable Model for Generative Dependency Parsing -- Dependency Parsing and Domain Adaptation with Data-Driven LR Models and Parser Ensembles -- Dependency Parsing Using Global Features -- Dependency Parsing with Second-Order Feature Maps and Annotated Semantic Information -- Strictly Lexicalised Dependency Parsing -- Favor Short Dependencies: Parsing with Soft and Hard Constraints on Dependency Length -- Corrective Dependency Parsing -- Inducing Lexicalised PCFGs with Latent Heads -- Self-Trained Bilexical Preferences to Improve Disambiguation Accuracy -- Are Very Large Context-Free Grammars Tractable? -- Efficiency in Unification-Based N-Best Parsing -- HPSG Parsing with a Supertagger -- Evaluating the Impact of Re-training a Lexical Disambiguation Model on Domain Adaptation of an HPSG Parser -- Semi-supervised Training of a Statistical Parser from Unlabeled Partially-Bracketed Data.
520 _aParsing technology is a central area of research in the automatic processing of human language. It is concerned with the decomposition of complex structures into their constituent parts, in particular with the methods, the tools and the software to parse automatically. Parsers are used in many application areas, such as information extraction from free text or speech, question answering, speech recognition and understanding, recommender systems, machine translation, and automatic summarization. New developments in the area of parsing technology are thus widely applicable. This book collects contributions from leading researchers in the area of natural language processing technology, describing their recent work and a range of new techniques and results. The book presents a state-of-the-art overview of current research in parsing tehcnologies with a focus on three important themes in the field today: dependency parsing, domain adaptation, and deep parsing. This book is the fourth in a line of such collections, and its breadth of coverage should make it suitable both as an overview of the state of the field for graduate students, and as a reference for established researchers in Computational Linguistics, Artificial Intelligence, Computer Science, Language Engineering, Information Science, and Cognitive Science. It will also be of interest to designers, developers, and advanced users of natural language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.
650 0 _aLinguistics.
650 0 _aTranslators (Computer programs).
650 0 _aComputational linguistics.
650 1 4 _aLinguistics.
650 2 4 _aComputational Linguistics.
650 2 4 _aLanguage Translation and Linguistics.
700 1 _aMerlo, Paola.
_eeditor.
700 1 _aNivre, Joakim.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9789048193516
830 0 _aText, Speech and Language Technology,
_x1386-291X ;
_v43
856 4 0 _uhttp://dx.doi.org/10.1007/978-90-481-9352-3
912 _aZDB-2-SHU
999 _c113635
_d113635