Editore: Morgan & Claypool Publishers, 2018
ISBN 10: 1681733943 ISBN 13: 9781681733944
Lingua: Inglese
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Editore: Morgan & Claypool Publishers, 2018
ISBN 10: 1681733927 ISBN 13: 9781681733920
Lingua: Inglese
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Editore: Springer International Publishing AG, Cham, 2018
ISBN 10: 3031007840 ISBN 13: 9783031007842
Lingua: Inglese
Da: Grand Eagle Retail, Mason, OH, U.S.A.
EUR 72,16
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including (1) entity recognition, typing and synonym discovery, (2) entity relation extraction, and (3) open-domain attribute-valuemining and information extraction. This book introduces this new research frontier and points out some promising research directions. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Editore: Springer International Publishing AG, Cham, 2018
ISBN 10: 3031007840 ISBN 13: 9783031007842
Lingua: Inglese
Da: AussieBookSeller, Truganina, VIC, Australia
EUR 119,57
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Aggiungi al carrelloPaperback. Condizione: new. Paperback. The real-world data, though massive, is largely unstructured, in the form of natural-language text. It is challenging but highly desirable to mine structures from massive text data, without extensive human annotation and labeling. In this book, we investigate the principles and methodologies of mining structures of factual knowledge (e.g., entities and their relationships) from massive, unstructured text corpora. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. This effort-light mining approach leads to a series of new principles and powerful methodologies for structuring text corpora, including (1) entity recognition, typing and synonym discovery, (2) entity relation extraction, and (3) open-domain attribute-valuemining and information extraction. This book introduces this new research frontier and points out some promising research directions. Departing from many existing structure extraction methods that have heavy reliance on human annotated data for model training, our effort-light approach leverages human-curated facts stored in external knowledge bases as distant supervision and exploits rich data redundancy in large text corpora for context understanding. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Editore: Morgan & Claypool Publishers, 2018
ISBN 10: 1681733943 ISBN 13: 9781681733944
Lingua: Inglese
Da: GoldBooks, Denver, CO, U.S.A.
EUR 222,36
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Da: Biblios, Frankfurt am main, HESSE, Germania
EUR 84,10
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Editore: Springer International Publishing, 2018
ISBN 10: 3031007840 ISBN 13: 9783031007842
Lingua: Inglese
Da: moluna, Greven, Germania
EUR 55,78
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Xiang Ren is an Assistant Professor in the Department of Computer Science at USC, affiliated faculty at USC ISI, and a part-time data science advisor at Snap Inc. At USC, Xiang is part of the Machine Learning Center, NLP community, and Center on Knowledge G.