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Grade 6.
Provides a dynamic network model of grammar that explains how linguistic structure is shaped by language use.
CD-ROM contains: Scripts for tutorials in text.
Grade 9.
"Networks of Language" will interest all those concerned with the acquisition and everyday operations of language, in particular scholars and advanced students in linguistics, psychology, and cognitive
This collection of research in on-line communication for second language learning inlcudes use of electronic mail, real-time writing and the World Wide Web. It analyses the theories underlying computer-assisted learning.
A comprehensive account of the neurobiological basis of language, arguing that species-specific brain differences may be at the root of the human capacity for language. Language makes us human. It is an intrinsic part of us, although we seldom think about it. Language is also an extremely complex entity with subcomponents responsible for its phonological, syntactic, and semantic aspects. In this landmark work, Angela Friederici offers a comprehensive account of these subcomponents and how they are integrated. Tracing the neurobiological basis of language across brain regions in humans and other primate species, she argues that species-specific brain differences may be at the root of the human capacity for language. Friederici shows which brain regions support the different language processes and, more important, how these brain regions are connected structurally and functionally to make language processes that take place in milliseconds possible. She finds that one particular brain structure (a white matter dorsal tract), connecting syntax-relevant brain regions, is present only in the mature human brain and only weakly present in other primate brains. Is this the “missing link” that explains humans' capacity for language? Friederici describes the basic language functions and their brain basis; the language networks connecting different language-related brain regions; the brain basis of language acquisition during early childhood and when learning a second language, proposing a neurocognitive model of the ontogeny of language; and the evolution of language and underlying neural constraints. She finds that it is the information exchange between the relevant brain regions, supported by the white matter tract, that is the crucial factor in both language development and evolution.
Neural networks are a family of powerful machine learning models. This book focuses on the application of neural network models to natural language data. The first half of the book (Parts I and II) covers the basics of supervised machine learning and feed-forward neural networks, the basics of working with machine learning over language data, and the use of vector-based rather than symbolic representations for words. It also covers the computation-graph abstraction, which allows to easily define and train arbitrary neural networks, and is the basis behind the design of contemporary neural network software libraries. The second part of the book (Parts III and IV) introduces more specialized neural network architectures, including 1D convolutional neural networks, recurrent neural networks, conditioned-generation models, and attention-based models. These architectures and techniques are the driving force behind state-of-the-art algorithms for machine translation, syntactic parsing, and many other applications. Finally, we also discuss tree-shaped networks, structured prediction, and the prospects of multi-task learning.