In grade school, no one would have ever guessed I'd grow up to become a linguist-- I was the kid who got Cs in French and couldn't produce a trill to save my life! I went to university majoring in civil engineering-- relieved that there was no language requirement for that major. But I ended up switching to geophysics, thinking that it would be less restrictive than engineering, and that it would allow me to spend more time in the mountains (which turned out to be wishful thinking)...Read more
The Cambridge Handbook of Communication Disorders examines the full range of developmental and acquired communication disorders and provides the most up-to-date and comprehensive guide to the epidemiology, aetiology and clinical features of these disorders.
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