000 | 04100nam a22005415i 4500 | ||
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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 |
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024 | 7 |
_a10.1007/978-3-031-02159-6 _2doi |
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050 | 4 | _aQ334-342 | |
050 | 4 | _aTA347.A78 | |
072 | 7 |
_aUYQ _2bicssc |
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_aCOM004000 _2bisacsh |
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_aUYQ _2thema |
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_a006.3 _223 |
100 | 1 |
_aHeinz, Jeffrey. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _987830 |
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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. |
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300 |
_aXXI, 139 p. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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347 |
_atext file _bPDF _2rda |
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490 | 1 |
_aSynthesis Lectures on Human Language Technologies, _x1947-4059 |
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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 |
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650 | 0 |
_aNatural language processing (Computer science). _94741 |
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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 |
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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 |