Linguistic structure prediction
Author(s)
Bibliographic Information
Linguistic structure prediction
（Synthesis lectures on human language technologies, 13）
Morgan & Claypool, c2011
- : pbk
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Note
Includes bibliographical references (p. 209-240) and index
Description and Table of Contents
Description
A 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.
Table of Contents
Representations and Linguistic Data
Decoding: Making Predictions
Learning Structure from Annotated Data
Learning Structure from Incomplete Data
Beyond Decoding: Inference
by "Nielsen BookData"