"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.
Syntactic Form and Discourse Function in Natural Language Generation
Users of natural languages have many word orders with which to encode the same truth-conditional meaning. They choose contextually appropriate strings from these many ways with little conscious effort and with effective communicative results. Previous computational models of when English speakers produce non-canonical word orders, like topicalization, left-dislocation, and clefts, fail-either by overgenerating these statistically rare forms or by undergenerating. The primary goal of this book is to present a better model of when speakers choose to produce certain non-canonical word orders by incorporating the effects of discourse context and speaker goals on syntactic choice. The theoretical model is then used as a basis for building a probabilistic classifier that can select the most human-like word order based on the surrounding discourse context. The model of discourse context used is a methodological advance both from a theoretical and an engineering perspective. It is built up from individual linguistic features, ones more easily and reliably annotated than the direct annotation of a discourse or rhetorical structure for a text. This book makes extensive use of previously unexamined naturally occurring corpus data of non-canonical word order in English, both to illustrate the points of the theoretical model and to train the statistical model.