|
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 |
|
|
Art of Java Web Development: Struts, Tapestry, Commons, Velocity, JUnit, Axis, Cocoon, InternetBeans, WebWork
A guide to the skills required for state-of-the-art web development, this book covers a variety of web development frameworks. The uses of the standard web API to create applications with increasingly sophisticated architectures are highlighted, and a discussion of the development of industry-accepted best practices for architecture is ... | | | | Coder to Developer: Tools and Strategies for Delivering Your SoftwareNo one can disparage the ability to write good code. At its highest levels, it is an art. But no one can confuse writing good code with developing good software. The difference—in terms of challenges, skills, and compensation—is immense.
Coder to Developer: Tools and Strategies for Delivering Your Software helps you... |
|