Shalom Lappin Livres

Exploring the intersection of deep learning and natural language processing, this book examines various deep learning systems applied to intriguing NLP tasks. It delves into how these advancements enhance our understanding of human linguistic acquisition and representation, offering insights into cognitive processes related to language.

The book offers a detailed critique of the economy-of-derivation model of grammar that has emerged within the framework of Chomsky's Minimalist Program. It looks at the conceptual and computational complexity problems as well as the empirical consequences of both global and local economy principles. The book compares the economy-of-derivation model with a local constraint model of grammar that does not invoke conditions on sets of derivations or on possible operations in a derivation. It argues that the pure local constraint model of grammar avoids the complexity problems resulting from economy-of-derivation principles and provides a more satisfactory explanation of the linguistic facts that economy theorists have cited in support of their approach. The local constraint model also allows for a more natural and empirically well-motivated grammatical architecture than the one postulated by the Minimalist Program.