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Building a Lexical Knowledge-Base of Near-Synonym Differences: Automatic Knowledge Acquisition - Softcover
Synopsis
Current natural language generation or machine translation systems cannot distinguish among near-synonyms - words that share the same core meaning but vary in their lexical nuances. This is due to a lack of knowledge about differences between near-synonyms in existing computational lexical resources. In this work, I automatically acquired a lexical knowledge-base of near-synonym differences from multiple sources, using an unsupervised decision- list algorithm. The main types of differences are: stylistic (for example, "inebriated" is more formal than "drunk"), attitudinal (for example, "skinny" is more pejorative than "slim"), and denotational (for example, "blunder" implies "accident" and "ignorance", while "error" does not). To show how the knowledge-base can be used in practice, I designed Xenon, a natural language generation system system that chooses the near-synonym that best matches a set of input preferences. I implemented Xenon by adding a near-synonym choice module and a near-synonym collocation module to an existing general-purpose surface realizer.
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About the Author
Diana Inkpen obtained her doctorate in 2004 from the University of Toronto, Department of Computer Science. She has a Masters in Computer Science and Engineering from the Technical University of Cluj-Napoca, Romania. Her research interests are in the areas of Computational Linguistics and Artificial Intelligence.
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Building a Lexical Knowledge-Base of Near-Synonym Differences
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LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838304772
ISBN 13: 9783838304779
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Current natural language generation or machine translation systems cannot distinguish among near-synonyms - words that share the same core meaning but vary in their lexical nuances. This is due to a lack of knowledge about differences between near-synonyms in existing computational lexical resources. In this work, I automatically acquired a lexical knowledge-base of near-synonym differences from multiple sources, using an unsupervised decision- list algorithm. The main types of differences are: stylistic (for example, 'inebriated' is more formal than 'drunk'), attitudinal (for example, 'skinny' is more pejorative than 'slim'), and denotational (for example, 'blunder' implies 'accident' and 'ignorance', while 'error' does not). To show how the knowledge-base can be used in practice, I designed Xenon, a natural language generation system system that chooses the near-synonym that best matches a set of input preferences. I implemented Xenon by adding a near-synonym choice module and a near-synonym collocation module to an existing general-purpose surface realizer. 192 pp. Englisch. Seller Inventory # 9783838304779
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Building a Lexical Knowledge-Base of Near-Synonym Differences : Automatic Knowledge Acquisition
Published by
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3838304772
ISBN 13: 9783838304779
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Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Current natural language generation or machine translation systems cannot distinguish among near-synonyms - words that share the same core meaning but vary in their lexical nuances. This is due to a lack of knowledge about differences between near-synonyms in existing computational lexical resources. In this work, I automatically acquired a lexical knowledge-base of near-synonym differences from multiple sources, using an unsupervised decision- list algorithm. The main types of differences are: stylistic (for example, 'inebriated' is more formal than 'drunk'), attitudinal (for example, 'skinny' is more pejorative than 'slim'), and denotational (for example, 'blunder' implies 'accident' and 'ignorance', while 'error' does not). To show how the knowledge-base can be used in practice, I designed Xenon, a natural language generation system system that chooses the near-synonym that best matches a set of input preferences. I implemented Xenon by adding a near-synonym choice module and a near-synonym collocation module to an existing general-purpose surface realizer. Seller Inventory # 9783838304779
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Building a Lexical Knowledge-Base of Near-Synonym Differences
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Inkpen DianaDiana Inkpen obtained her doctorate in 2004 from the University of Toronto, Department of Computer Science. She has a Masters in Computer Science and Engineering from the Technical University of Cluj-Napoca, Romania. Her. Seller Inventory # 5411205
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Building a Lexical Knowledge-Base of Near-Synonym Differences | Automatic Knowledge Acquisition
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ISBN 13: 9783838304779
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Taschenbuch. Condition: Neu. Building a Lexical Knowledge-Base of Near-Synonym Differences | Automatic Knowledge Acquisition | Diana Inkpen | Taschenbuch | 192 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838304779 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Seller Inventory # 101509118
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Building a Lexical Knowledge-Base of Near-Synonym Differences
Published by
LAP LAMBERT Academic Publishing Mai 2010, 2010
ISBN 10: 3838304772
ISBN 13: 9783838304779
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Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Current natural language generation or machine translation systems cannot distinguish among near-synonyms - words that share the same core meaning but vary in their lexical nuances. This is due to a lack of knowledge about differences between near-synonyms in existing computational lexical resources. In this work, I automatically acquired a lexical knowledge-base of near-synonym differences from multiple sources, using an unsupervised decision- list algorithm. The main types of differences are: stylistic (for example, 'inebriated' is more formal than 'drunk'), attitudinal (for example, 'skinny' is more pejorative than 'slim'), and denotational (for example, 'blunder' implies 'accident' and 'ignorance', while 'error' does not). To show how the knowledge-base can be used in practice, I designed Xenon, a natural language generation system system that chooses the near-synonym that best matches a set of input preferences. I implemented Xenon by adding a near-synonym choice module and a near-synonym collocation module to an existing general-purpose surface realizer.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 192 pp. Englisch. Seller Inventory # 9783838304779
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Building a Lexical Knowledge-Base of Near-Synonym Differences: Automatic Knowledge Acquisition
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ISBN 13: 9783838304779
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