Kooperativer Bibliotheksverbund

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Format: 1 online resource (xiv, 417 pages) : , digital, PDF file(s).

ISBN: 9781139194655 (ebook)

Content: The problem of inducing, learning or inferring grammars has been studied for decades, but only in recent years has grammatical inference emerged as an independent field with connections to many scientific disciplines, including bio-informatics, computational linguistics and pattern recognition. This book meets the need for a comprehensive and unified summary of the basic techniques and results, suitable for researchers working in these various areas. In Part I, the objects of use for grammatical inference are studied in detail: strings and their topology, automata and grammars, whether probabilistic or not. Part II carefully explores the main questions in the field: What does learning mean? How can we associate complexity theory with learning? In Part III the author describes a number of techniques and algorithms that allow us to learn from text, from an informant, or through interaction with the environment. These concern automata, grammars, rewriting systems, pattern languages or transducers.

Note: Title from publisher's bibliographic system (viewed on 01 Feb 2016).

Additional Edition: Print version: ISBN 9780521763165

Language: English

Subjects: Computer Science , Comparative Studies. Non-European Languages/Literatures

URL: https://doi.org/10.1017/CBO9781139194655

URL: https://doi.org/10.1017/CBO9781139194655

URL: Volltext  (lizenzpflichtig)

URL: Inhaltstext

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Format: 1 Online-Ressource (xxi, 139 Seiten)

Edition: First edition

Edition: Also available in print

ISBN: 1608459780 , 9781608459780

Series Statement: Synthesis lectures on human language technologies #28

Content: This 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

Content: 1. Studying learning -- 1.1 An overview of grammatical inference -- 1.2 Formal and empirical grammatical inference -- 1.3 Formal grammatical inference -- 1.3.1 Language and grammar -- 1.3.2 Language families -- 1.3.3 Learning languages efficiently -- 1.4 Empirical grammatical inference -- 1.4.1 Languages, grammars, and language families -- 1.4.2 Evaluation -- 1.5 Summary -- 1.6 Formal preliminaries --

Content: 2. Formal learning -- 2.1 Introduction -- 2.1.1 The issues of learning -- 2.1.2 Learning scenarios -- 2.1.3 Learning grammars of languages -- 2.2 Learnability: definitions and paradigms -- 2.2.1 Blame the data, not the algorithm -- 2.2.2 A non-probabilistic setting: identification in the limit -- 2.2.3 An active learning setting -- 2.2.4 Introducing complexity -- 2.2.5 A probabilistic version of identification in the limit -- 2.2.6 Probably approximately correct (PAC) learning -- 2.3 Grammar formalisms -- 2.3.1 Finite-state machines recognizing strings -- 2.3.2 Probabilistic finite-state machines -- 2.3.3 Transducers -- 2.3.4 More complex formalisms -- 2.3.5 Dealing with trees and graphs -- 2.4 Is grammatical inference an instance of machine learning? -- 2.5 Summary --

Content: 3. Learning regular languages -- 3.1 Introduction -- 3.2 Bias selection reduces the problem space -- 3.3 Regular grammars -- 3.4 State-merging algorithms -- 3.4.1 The problem of learning stress patterns -- 3.4.2 Merging states -- 3.4.3 Finite-state representations of finite samples -- 3.4.4 The state-merging theorem -- 3.5 State-merging as a learning bias -- 3.6 State-merging as inference rules -- 3.7 RPNI -- 3.7.1 How it works -- 3.7.2 Theoretical results -- 3.8 Regular relations -- 3.9 Learning stochastic regular languages -- 3.9.1 Stochastic languages -- 3.9.2 Structure of the class is deterministic and known a priori -- 3.9.3 Structure of the class is deterministic but not known a priori -- 3.9.4 Structure of the class is non-deterministic and not known a priori -- 3.10 Summary --

Content: 4. Learning non-regular languages -- 4.1 Substitutability -- 4.1.1 Identifying structure -- 4.1.2 Learning using substitutability -- 4.2 Empirical approaches -- 4.2.1 Expanding and reducing approaches -- 4.2.2 Supervised and unsupervised approaches -- 4.2.3 Word-based and POS-based approaches -- 4.2.4 Description of empirical systems -- 4.2.5 Comparison of empirical systems -- 4.3 Issues for evaluation -- 4.3.1 Looks-good-to-me approach -- 4.3.2 Rebuilding known grammars -- 4.3.3 Compare against a treebank -- 4.3.4 Language membership -- 4.4 Formal approaches -- 4.5 Summary --

Content: 5. Lessons learned and open problems -- 5.1 Summary -- 5.2 Lessons -- 5.3 Problems -- 5.3.1 Learning targets -- 5.3.2 Learning criteria -- 5.4 Resources -- 5.5 Final words -- Bibliography -- Author biographies

Note: Includes bibliographical references (pages 121-136) , Also available in print. , System requirements: Adobe Acrobat Reader. , Mode of access: World Wide Web.

Additional Edition: ISBN 1608459772

Additional Edition: ISBN 9781608459773

Additional Edition: Print version Heinz, Jeffrey Grammatical inference for computational linguistics San Rafael : Morgan & Claypool, 2014

Language: English

Subjects: Computer Science

Keywords: Grammatik ; Maschinelles Lernen

URL: Volltext

Author information: Heinz, Jeffrey 1974-

UID:

Format: Online-Ressource (xxi, 139 pages) , illustrations.

ISBN: 9781608459780

Series Statement: Synthesis lectures on human language technologies # 28

Content: This 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.

Note: Part of: Synthesis digital library of engineering and computer science. - Includes bibliographical references (pages 121-136). - Compendex. INSPEC. Google scholar. Google book search. - Title from PDF title page (viewed on November 24, 2015) , 1. Studying learning -- 1.1 An overview of grammatical inference -- 1.2 Formal and empirical grammatical inference -- 1.3 Formal grammatical inference -- 1.3.1 Language and grammar -- 1.3.2 Language families -- 1.3.3 Learning languages efficiently -- 1.4 Empirical grammatical inference -- 1.4.1 Languages, grammars, and language families -- 1.4.2 Evaluation -- 1.5 Summary -- 1.6 Formal preliminaries -- , Mode of access: World Wide Web. , System requirements: Adobe Acrobat Reader.

Additional Edition: ISBN 9781608459773

Additional Edition: Erscheint auch als Heinz, Jeffrey, 1974 - Grammatical inference for computational linguistics San Rafael : Morgan & Claypool, 2015 ISBN 9781608459773

Language: English

Keywords: Computerlinguistik ; Grammatik

DOI: 10.2200/S00643ED1V01Y201504HLT028

URL: Volltext

URL: Volltext

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Format: XIII, 416 S. : , graph. Darst.

Edition: 1. publ.

ISBN: 978-0-521-76316-5

Language: English

Subjects: Comparative Studies. Non-European Languages/Literatures

Keywords: Maschinelles Lernen ; Formale Grammatik ; Inferenz

URL: http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018986762&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA

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Format: 1 online resource (437 p.)

ISBN: 9783050062365 , 9783110238570

Series Statement: Studia grammatica , 72

Content: This volume contributes to a linguistic program characterized by the view that explanatory goals in syntax and semantics can be met only in models that are sufficiently formalized. The properties of these formalizations must be well understood, and they have to do justice to both the syntactic and semantic aspects of a construction. The contributions shed light on this view from the perspectives of theoretical linguistics (semantics, syntax), automata theory, and computational and mathematical linguistics.

Note: Front Matter -- , I Syntax -- , A Spurious Genitive Puzzle in Polish -- , Semantic Type Effects on Crossing Movement in German. -- , Me and Chomsky -- , On the Typology of Verb Second -- , Movement from Verb-Second Clauses Revisited -- , Spurious Ambiguities and the Parentheticals Debate -- , II Semantics -- , On Squeamishness of the Royal Kind -- , Information Structure of schon -- , When-Clauses, Factive Verbs and Correlates -- , The Proof Theory of Partial Variables -- , Brentano's Apple -- , How to Interpret "Expletive" Negation under bevor in German -- , Wide Scope in situ -- , What it Takes to be Missing -- , III Automata Theory -- , Robust Parsing as a Constraint Optimization Problem within a Finite-state Approach -- , ε-Removal by Loop Reduction for Finite-state Automata -- , Efficient Online k-Best Lookup in Weighted Finite-State Cascades -- , Tomita's Algorithm Revisited -- , IV Mathematical Linguistics -- , On the Treatment of Multiple-Wh-Interrogatives in Minimalist Grammars -- , Some Remarks on Mildly Context-Sensitive Copying -- , V Computational Linguistics -- , On Statistical Metrics for Selection and Phrasality -- , Testing the Distributional Hypothesis for Collaborative Tagging Systems. -- , VI Classical Studies -- , Herstellungstechniken von Inschriften auf römischen Wasserleitungsrohren aus Blei , Issued also in print. , Mode of access: Internet via World Wide Web. , In English.

In: DGBA Backlist Complete English Language 2000-2014 PART1, De Gruyter, 9783110238570

In: DGBA Backlist Linguistics and Semiotics 2000-2014 (EN), De Gruyter, 9783110238457

In: DGBA Linguistics and Semiotics 2000 - 2014, De Gruyter, 9783110636970

In: eBook-Paket OWV/AV  Sprachwissenschaft 2005-2012, De Gruyter, 9783110346824

Additional Edition: ISBN 9783050049311

Language: English

DOI: 10.1524/9783050062365

URL: https://doi.org/10.1524/9783050062365

URL: https://www.degruyter.com/isbn/9783050062365

URL: Cover

UID:

Format: Online-Ressource

ISBN: 9783540706786

Series Statement: Lecture Notes in Computer Science 1147

Additional Edition: ISBN 9783540617785

Additional Edition: Buchausg. u.d.T. Grammatical inference Berlin : Springer, 1996 ISBN 3540617787

Language: English

Subjects: Computer Science

Keywords: Formale Grammatik ; Maschinelles Lernen ; Induktion ; Konferenzschrift

DOI: 10.1007/BFb0033338

URL: Volltext

UID:

Format: 139 S.

Edition: First edition

ISBN: 9781608459773

Series Statement: Synthesis lectures on human language technologies 32

Additional Edition: ISBN 9781608459780

Additional Edition: Erscheint auch als Online-Ausgabe Heinz, Jeffrey Grammatical inference for computational linguistics San Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, 2016 ISBN 9781608459780

Language: English

Keywords: Computerlinguistik ; Grammatik

Author information: Heinz, Jeffrey 1974-