"Buenos dias", "buenas noches" -- this was the first words in a foreign language I heard in my life, as a three-year old boy growing up in developing post-war Western Germany, where the first gastarbeiters had arrived from Spain. Fascinated by the strange sounds, I tried to get to know some more languages, the only opportunity being TV courses of English and French -- there was no foreign language education for pre-teen school children in Germany yet in those days. Read more
To find some answers Tim Machan explores the language's present and past, and looks ahead to its futures among the one and a half billion people who speak it. His search is fascinating and important, for definitions of English have influenced education and law in many countries and helped shape the identities of those who live in them.
This volume provides a new perspective on the evolution of the special language of medicine, based on the electronic corpus of Early Modern English Medical Texts, containing over two million words of medical writing from 1500 to 1700.
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