Articles liés à Linguistic Structure Prediction
Synopsis
A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference
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À propos de l?auteur
Noah A. Smith is an assistant professor in the Language Technologies Institute and Machine Learning Department at the School of Computer Science at Carnegie Mellon University. He received his Ph.D. in Computer Science from Johns Hopkins University (2006) and his B.S. in Computer Science and B.A. in Linguistics from the University of Maryland (2001). He was awarded a Hertz Foundation fellowship (2001-2006), served on the DARPA Computer Science Study Panel (2007) and the editorial board of the journal Computational Linguistics, and received a best paper award from the Association for Computational Linguistics (2009) and an NSF CAREER grant (2011). His research interests include statistical natural language processing, especially unsupervised methods, machine learning for structured data, and applications of natural language processing.
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- ÉditeurSpringer
- Date d'édition2011
- ISBN 10 3031010159
- ISBN 13 9783031010156
- ReliureBroché
- Langueanglais
- Nombre de pages272
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Linguistic Structure Prediction
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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of ling. N° de réf. du vendeur 608129266
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Linguistic Structure Prediction
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Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference. N° de réf. du vendeur 9783031010156
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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference 272 pp. Englisch. N° de réf. du vendeur 9783031010156
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Linguistic Structure Prediction
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Springer, 2011
ISBN 10 : 3031010159
ISBN 13 : 9783031010156
Ancien ou d'occasion
Couverture souple
Vendeur : GreatBookPricesUK, Woodford Green, Royaume-Uni
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