Improving dependency parsing using word clusters - Couverture souple

Lien, Jostein

9783659794551: Improving dependency parsing using word clusters

Couverture souple

ISBN 10 : 3659794554 ISBN 13 : 9783659794551

Editeur : LAP Lambert Academic Publishing, 2015

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Several studies have attempted to improve the accuracy in dependency parsing by including information about word clusters into the parsing models. The use of word clusters are typically motivated by the shortage of labeled training data and domain adaption, attempting to influence a parsing model for use on data from a new domain. This book shows the effect of using cluster-based features in MaltParser, a data-driven parser for inductive dependency parsing. Different clustering features are used for generating clusters, using the K-means clustering algorithm. The clusters are used as a source of additional information in an expanded feature model used by the MaltParser system. Parsing experiments are performed on several different data sets, including the Wall Street Journal and texts from various web domains. Significantly improved parsing results are reported when using a cluster-informed parser compared to the baseline parser. The contents of this book might be of interest to anyone interested in the application of machine learning in language technology.

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� propos de l'auteur

Jostein Lien, MSc: Studied Informatics: Language and Communication at the University of Oslo.

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�diteur: LAP Lambert Academic Publishing
Date d'�dition: 2015
Langue: anglais
ISBN 10: 3659794554
ISBN 13: 9783659794551
Reliure: Broch�
Nombre de pages: 120
Coordonn�es du fabricant: non disponible
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Improving dependency parsing using word clusters

Jostein Lien

Edit� par LAP LAMBERT Academic Publishing Okt 2015, 2015

ISBN 10 : 3659794554 ISBN 13 : 9783659794551

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Several studies have attempted to improve the accuracy in dependency parsing by including information about word clusters into the parsing models. The use of word clusters are typically motivated by the shortage of labeled training data and domain adaption, attempting to influence a parsing model for use on data from a new domain. This book shows the effect of using cluster-based features in MaltParser, a data-driven parser for inductive dependency parsing. Different clustering features are used for generating clusters, using the K-means clustering algorithm. The clusters are used as a source of additional information in an expanded feature model used by the MaltParser system. Parsing experiments are performed on several different data sets, including the Wall Street Journal and texts from various web domains. Significantly improved parsing results are reported when using a cluster-informed parser compared to the baseline parser. The contents of this book might be of interest to anyone interested in the application of machine learning in language technology. 120 pp. Englisch. N� de r�f. du vendeur 9783659794551

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Improving dependency parsing using word clusters

Jostein Lien

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ISBN 10 : 3659794554 ISBN 13 : 9783659794551

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Lien JosteinJostein Lien, MSc: Studied Informatics: Language and Communication at the University of Oslo.Several studies have attempted to improve the accuracy in dependency parsing by including information about word clusters in. N� de r�f. du vendeur 158605542

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Improving dependency parsing using word clusters

Jostein Lien

Edit� par LAP LAMBERT Academic Publishing Okt 2015, 2015

ISBN 10 : 3659794554 ISBN 13 : 9783659794551

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Several studies have attempted to improve the accuracy in dependency parsing by including information about word clusters into the parsing models. The use of word clusters are typically motivated by the shortage of labeled training data and domain adaption, attempting to influence a parsing model for use on data from a new domain. This book shows the effect of using cluster-based features in MaltParser, a data-driven parser for inductive dependency parsing. Different clustering features are used for generating clusters, using the K-means clustering algorithm. The clusters are used as a source of additional information in an expanded feature model used by the MaltParser system. Parsing experiments are performed on several different data sets, including the Wall Street Journal and texts from various web domains. Significantly improved parsing results are reported when using a cluster-informed parser compared to the baseline parser. The contents of this book might be of interest to anyone interested in the application of machine learning in language technology.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 120 pp. Englisch. N� de r�f. du vendeur 9783659794551

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Improving dependency parsing using word clusters

Jostein Lien

Edit� par LAP LAMBERT Academic Publishing, 2015

ISBN 10 : 3659794554 ISBN 13 : 9783659794551

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Several studies have attempted to improve the accuracy in dependency parsing by including information about word clusters into the parsing models. The use of word clusters are typically motivated by the shortage of labeled training data and domain adaption, attempting to influence a parsing model for use on data from a new domain. This book shows the effect of using cluster-based features in MaltParser, a data-driven parser for inductive dependency parsing. Different clustering features are used for generating clusters, using the K-means clustering algorithm. The clusters are used as a source of additional information in an expanded feature model used by the MaltParser system. Parsing experiments are performed on several different data sets, including the Wall Street Journal and texts from various web domains. Significantly improved parsing results are reported when using a cluster-informed parser compared to the baseline parser. The contents of this book might be of interest to anyone interested in the application of machine learning in language technology. N� de r�f. du vendeur 9783659794551

Contacter le vendeur