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007			cr nn 008mamaa
008			220601s2016 sz | s |||| 0|eng d
020			_a9783031021596
024	7		_a10.1007/978-3-031-02159-6 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aP98 _b2016 EB
100	1		_aHeinz, Jeffrey, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687396 _d1974-
245	1	0	_aGrammatical Inference for Computational Linguistics _cby Jeffrey Heinz, Colin de la Higuera, Menno van Zaanen
250			_a1st edition 2016
264		1	_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XXI, 139 páginas)
336			_atexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_aarchivo de texto _bPDF
490	0		_aSynthesis Lectures on Human Language Technologies _x1947-4059
505	0		_aList of Figures -- List of Tables -- Preface -- Studying Learning -- Formal Learning -- Learning Regular Languages -- Learning Non-Regular Languages -- Lessons Learned and Open Problems -- Bibliography -- Author Biographies.
520			_aThis book provides a thorough introduction to the subfield of theoretical computer science known as grammatical inference from a computational linguistic perspective. Grammatical inference provides principled methods for developing computationally sound algorithms that learn structure from strings of symbols. The relationship to computational linguistics is natural because many research problems in computational linguistics are learning problems on words, phrases, and sentences: What algorithm can take as input some finite amount of data (for instance a corpus, annotated or otherwise) and output a system that behaves "correctly" on specific tasks? Throughout the text, the key concepts of grammatical inference are interleaved with illustrative examples drawn from problems in computational linguistics. Special attention is paid to the notion of "learning bias." In the context of computational linguistics, such bias can be thought to reflect common (ideally universal) properties of natural languages. This bias can be incorporated either by identifying a learnable class of languages which contains the language to be learned or by using particular strategies for optimizing parameter values. Examples are drawn largely from two linguistic domains (phonology and syntax) which span major regions of the Chomsky Hierarchy (from regular to context-sensitive classes). The conclusion summarizes the major lessons and open questions that grammatical inference brings to computational linguistics. Table of Contents: List of Figures / List of Tables / Preface / Studying Learning / Formal Learning / Learning Regular Languages / Learning Non-Regular Languages / Lessons Learned and Open Problems / Bibliography / Author Biographies.
988			_aSynthesis Collection of Technology_2016
700	1		_aDe la Higuera, Colin _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687397
700	1		_aZaanen, Menno van, _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9687398 _d1972-
776	0	8	_iPrinted edition: _z9783031010316
776	0	8	_iPrinted edition: _z9783031032875
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-031-02159-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2023 _dz _esc _zSI