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.

A textbook exploring predictive modes of linguistic development and analysis.

During the last two decades, computational linguists, in concert with other researchers in AI, have turned to machine learning and statistical techniques to capture features of natural language and aspects of the learning process that are not easily accommodated in classical algebraic frameworks. These developments are producing a revolution in linguistics in which traditional symbolic systems are giving way to probabilistic and deep learning approaches. This collection features articles that provide background to these approaches, and their application in syntax, semantics, pragmatics, morphology, psycholinguistics, neurolinguistics, and dialogue modeling. Each chapter provides a self-contained introduction to the topic that it covers, making this volume accessible to graduate students and researchers in linguistics, NLP, AI, and cognitive science.