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Chaitanya Chemudugunta

Published by LAP Lambert Academic Publishing 2010-09-14 (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Academic Publishing 2010-09-14, 2010. paperback. Book Condition: New. Bookseller Inventory # 9783838364100

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Chaitanya Chemudugunta

Published by LAP Lambert Academic Publishing (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Academic Publishing, 2010. PAP. Book Condition: New. New Book. Shipped from US within 10 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bookseller Inventory # IQ-9783838364100

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Chemudugunta, Chaitanya

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: Book Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Applications in Information Retrieval and Concept Modeling | Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this book, we describe how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the special-words topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a special-words distribution specific to the document, and (iii) a corpus-level background distribution. We describe the utility of the special-words topic models for information retrieval tasks. We next describe the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine data-driven topics and semantic concepts, we describe the concept-topic model and the hierarchical concept-topic model which represent a document as a distribution over data-driven topics and semantic concepts. | Format: Paperback | Language/Sprache: english | 199 gr | 220x150x7 mm | 140 pp. Bookseller Inventory # K9783838364100

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Chemudugunta, Chaitanya

Published by LAP Lambert Academic Publishing (2016)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Academic Publishing, 2016. Paperback. Book Condition: New. PRINT ON DEMAND Book; New; Publication Year 2016; Not Signed; Fast Shipping from the UK. No. book. Bookseller Inventory # ria9783838364100_lsuk

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Chaitanya Chemudugunta

Published by LAP Lambert Academic Publishing (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Academic Publishing, 2010. PAP. Book Condition: New. New Book. Delivered from our UK warehouse in 3 to 5 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Bookseller Inventory # LQ-9783838364100

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Chaitanya Chemudugunta

Published by LAP LAMBERT Academic Publishing (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP LAMBERT Academic Publishing, 2010. Paperback. Book Condition: New. book. Bookseller Inventory # M3838364104

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Chemudugunta, Chaitanya

Published by LAP LAMBERT Academic Publishin (2017)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP LAMBERT Academic Publishin, 2017. Paperback. Book Condition: New. Never used! This item is printed on demand. Bookseller Inventory # 3838364104

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Chaitanya Chemudugunta

Published by LAP Lambert Acad. Publ. Sep 2010 (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Acad. Publ. Sep 2010, 2010. Taschenbuch. Book Condition: Neu. Neuware - Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this book, we describe how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the special-words topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a special-words distribution specific to the document, and (iii) a corpus-level background distribution. We describe the utility of the special-words topic models for information retrieval tasks. We next describe the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine data-driven topics and semantic concepts, we describe the concept-topic model and the hierarchical concept-topic model which represent a document as a distribution over data-driven topics and semantic concepts. 140 pp. Englisch. Bookseller Inventory # 9783838364100

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Chaitanya Chemudugunta

Published by LAP Lambert Acad. Publ. Sep 2010 (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Acad. Publ. Sep 2010, 2010. Taschenbuch. Book Condition: Neu. Neuware - Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this book, we describe how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the special-words topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a special-words distribution specific to the document, and (iii) a corpus-level background distribution. We describe the utility of the special-words topic models for information retrieval tasks. We next describe the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine data-driven topics and semantic concepts, we describe the concept-topic model and the hierarchical concept-topic model which represent a document as a distribution over data-driven topics and semantic concepts. 140 pp. Englisch. Bookseller Inventory # 9783838364100

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Chaitanya Chemudugunta

Published by LAP LAMBERT Academic Publishing (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP LAMBERT Academic Publishing, 2010. Paperback. Book Condition: Used: Good. This item is printed on demand. Bookseller Inventory # SONG3838364104

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Chemudugunta, Chaitanya

Published by LAP LAMBERT Academic Publishin (2017)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP LAMBERT Academic Publishin, 2017. Paperback. Book Condition: New. Never used! This item is printed on demand. Bookseller Inventory # P113838364104

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Chemudugunta, Chaitanya

Published by LAP LAMBERT Academic Publishin (2017)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP LAMBERT Academic Publishin, 2017. Paperback. Book Condition: Like New. Almost new condition. This item is printed on demand. Bookseller Inventory # P013838364104

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Chaitanya Chemudugunta

Published by LAP Lambert Acad. Publ. Sep 2010 (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Acad. Publ. Sep 2010, 2010. Taschenbuch. Book Condition: Neu. This item is printed on demand - Print on Demand Neuware - Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this book, we describe how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the special-words topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a special-words distribution specific to the document, and (iii) a corpus-level background distribution. We describe the utility of the special-words topic models for information retrieval tasks. We next describe the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine data-driven topics and semantic concepts, we describe the concept-topic model and the hierarchical concept-topic model which represent a document as a distribution over data-driven topics and semantic concepts. 140 pp. Englisch. Bookseller Inventory # 9783838364100

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Chemudugunta, Chaitanya

Published by LAP LAMBERT Academic Publishin (2017)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP LAMBERT Academic Publishin, 2017. Paperback. Book Condition: Very Good. Great condition with minimal wear, aging, or shelf wear. This item is printed on demand. Bookseller Inventory # P023838364104

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Chaitanya Chemudugunta

Published by LAP Lambert Academic Publishing, Germany (2010)

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Academic Publishing, Germany, 2010. Paperback. Book Condition: New. Language: English . Brand New Book. Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this book, we describe how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the special-words topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a special-words distribution specific to the document, and (iii) a corpus-level background distribution. We describe the utility of the special-words topic models for information retrieval tasks. We next describe the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine data-driven topics and semantic concepts, we describe the concept-topic model and the hierarchical concept-topic model which represent a document as a distribution over data-driven topics and semantic concepts. Bookseller Inventory # KNV9783838364100

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Chaitanya Chemudugunta

Published by LAP Lambert Academic Publishing

ISBN 10: 3838364104 ISBN 13: 9783838364100

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Item Description: LAP Lambert Academic Publishing. Paperback. Book Condition: New. Paperback. 140 pages. Dimensions: 8.7in. x 6.0in. x 0.5in.Statistical topic models are a class of probabilistic latent variable models for textual data that represent text documents as distributions over topics. These models have been shown to produce interpretable summarization of documents in the form of topics. In this book, we describe how the statistical topic modeling framework can be used for information retrieval tasks and for the integration of background knowledge in the form of semantic concepts. We first describe the special-words topic models in which a document is represented as a distribution of (i) a mixture of shared topics, (ii) a special-words distribution specific to the document, and (iii) a corpus-level background distribution. We describe the utility of the special-words topic models for information retrieval tasks. We next describe the problem of integrating background knowledge in the form of semantic concepts into the topic modeling framework. To combine data-driven topics and semantic concepts, we describe the concept-topic model and the hierarchical concept-topic model which represent a document as a distribution over data-driven topics and semantic concepts. This item ships from multiple locations. Your book may arrive from Roseburg,OR, La Vergne,TN. Paperback. Bookseller Inventory # 9783838364100

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