000			04100nam a22005415i 4500
001			978-3-031-02159-6
003			DE-He213
005			20240730165211.0
007			cr nn 008mamaa
008			220601s2016 sz | s |||| 0|eng d
020			_a9783031021596 _9978-3-031-02159-6
024	7		_a10.1007/978-3-031-02159-6 _2doi
050		4	_aQ334-342
050		4	_aTA347.A78
072		7	_aUYQ _2bicssc
072		7	_aCOM004000 _2bisacsh
072		7	_aUYQ _2thema
082	0	4	_a006.3 _223
100	1		_aHeinz, Jeffrey. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987830
245	1	0	_aGrammatical Inference for Computational Linguistics _h[electronic resource] / _cby Jeffrey Heinz, Colin de la Higuera, Menno van Zaanen.
250			_a1st ed. 2016.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016.
300			_aXXI, 139 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_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.
650		0	_aArtificial intelligence. _93407
650		0	_aNatural language processing (Computer science). _94741
650		0	_aComputational linguistics. _96146
650	1	4	_aArtificial Intelligence. _93407
650	2	4	_aNatural Language Processing (NLP). _931587
650	2	4	_aComputational Linguistics. _96146
700	1		_aHiguera, Colin de la. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987834
700	1		_aZaanen, Menno van. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987835
710	2		_aSpringerLink (Online service) _987837
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031010316
776	0	8	_iPrinted edition: _z9783031032875
830		0	_aSynthesis Lectures on Human Language Technologies, _x1947-4059 _987839
856	4	0	_uhttps://doi.org/10.1007/978-3-031-02159-6
912			_aZDB-2-SXSC
942			_cEBK
999			_c86157 _d86157