The Experts below are selected from a list of 166584 Experts worldwide ranked by ideXlab platform
Philippe Blache - One of the best experts on this subject based on the ideXlab platform.
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Fuzziness and variability in natural language processing
IEEE International Conference on Fuzzy Systems, 2017Co-Authors: A.t. Urrutià, M.d.j. López, Philippe BlacheAbstract:© 2017 IEEE. This paper aims to establish a link between Linguistics and fuzzy phenomena. Throughout the history, Linguistics has been relying on discrete descriptions to explain Natural Language Processing. This fact has a negative impact in various areas of language and technology since Linguistics rejects all the inputs which are out from a discrete rule. Although this paper might approach the subject more from a linguistic than from a mathematical point of view, we present the theoretical considerations and reasoning used in elaborating a formal characterization of fuzziness in natural language grammars. Property Grammars will be used as the formal theory in order to explain Natural Language fuzziness and variability.
Benedikt Szmrecsanyi - One of the best experts on this subject based on the ideXlab platform.
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Linguistic Complexity: Second Language Acquisition, Indigenization, Contact - Linguistic Complexity: Second Language Acquisition, Indigenization, Contact
2012Co-Authors: Bernd Kortmann, Benedikt SzmrecsanyiAbstract:Linguistic complexity is one of the currently most hotly debated notions in Linguistics. The essays in this volume address the intricacies of assessing the complexity of languages and language varieties (here: of English) in three major linguistic disciplines: creolistics, indigenization and nativization studies, and Second Language Acquisition research. The volume is thus unique in bringing together leading representatives of three often disjunct fields of linguistic scholarship in which linguistic complexity is seen as a dynamic and inherently variable parameter.
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linguistic complexity second language acquisition indigenization contact
2012Co-Authors: Bernd Kortmann, Benedikt SzmrecsanyiAbstract:Linguistic complexity is one of the currently most hotly debated notions in Linguistics. The essays in this volume address the intricacies of assessing the complexity of languages and language varieties (here: of English) in three major linguistic disciplines: creolistics, indigenization and nativization studies, and Second Language Acquisition research. The volume is thus unique in bringing together leading representatives of three often disjunct fields of linguistic scholarship in which linguistic complexity is seen as a dynamic and inherently variable parameter.
A.t. Urrutià - One of the best experts on this subject based on the ideXlab platform.
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Fuzziness and variability in natural language processing
IEEE International Conference on Fuzzy Systems, 2017Co-Authors: A.t. Urrutià, M.d.j. López, Philippe BlacheAbstract:© 2017 IEEE. This paper aims to establish a link between Linguistics and fuzzy phenomena. Throughout the history, Linguistics has been relying on discrete descriptions to explain Natural Language Processing. This fact has a negative impact in various areas of language and technology since Linguistics rejects all the inputs which are out from a discrete rule. Although this paper might approach the subject more from a linguistic than from a mathematical point of view, we present the theoretical considerations and reasoning used in elaborating a formal characterization of fuzziness in natural language grammars. Property Grammars will be used as the formal theory in order to explain Natural Language fuzziness and variability.
Menno Van Zaanen - One of the best experts on this subject based on the ideXlab platform.
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Grammatical Inference for Computational Linguistics
Grammatical Inference for Computational Linguistics, 1Co-Authors: Jeffrey Heinz, Colin Colin De La Higuera, Menno Van ZaanenAbstract: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.
M.d.j. López - One of the best experts on this subject based on the ideXlab platform.
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Fuzziness and variability in natural language processing
IEEE International Conference on Fuzzy Systems, 2017Co-Authors: A.t. Urrutià, M.d.j. López, Philippe BlacheAbstract:© 2017 IEEE. This paper aims to establish a link between Linguistics and fuzzy phenomena. Throughout the history, Linguistics has been relying on discrete descriptions to explain Natural Language Processing. This fact has a negative impact in various areas of language and technology since Linguistics rejects all the inputs which are out from a discrete rule. Although this paper might approach the subject more from a linguistic than from a mathematical point of view, we present the theoretical considerations and reasoning used in elaborating a formal characterization of fuzziness in natural language grammars. Property Grammars will be used as the formal theory in order to explain Natural Language fuzziness and variability.
