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OverviewA 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. Full Product DetailsAuthor: Noah A. Smith , Graeme HirstPublisher: Morgan & Claypool Publishers Imprint: Morgan & Claypool Publishers Edition: annotated edition Dimensions: Width: 18.70cm , Height: 1.40cm , Length: 23.50cm Weight: 0.470kg ISBN: 9781608454051ISBN 10: 1608454053 Pages: 274 Publication Date: 30 June 2011 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Out of print, replaced by POD  We will order this item for you from a manufatured on demand supplier. Table of ContentsRepresentations and Linguistic Data Decoding: Making Predictions Learning Structure from Annotated Data Learning Structure from Incomplete Data Beyond Decoding: InferenceReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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