Fr. 195.00

Nanosyntax and the Lexicalization Algorithm

English · Hardback

Will be released 11.09.2025

Description

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This book presents the latest research in the Nanosyntax framework, a late-insertion theory based on the idea that the elementary building blocks of syntactic trees are limited to single features. The chapters contribute to a better understanding of the framework as a whole and specifically of the lexicalization algorithm.

About the author










Pavel Caha is an associate professor at Masaryk University. His research focuses on the theoretical implications of case marking, declensions, and degree morphology. His work has been published in journals such as Natural Language & Linguistic Theory, Morphology, Journal of Linguistics, and Glossa, and in reference works including The Oxford Research Encyclopedia of Linguistics, The Wiley-Blackwell Companion to Morphology, and The Cambridge Handbook of Distributed Morphology.

Karen De Clercq is a CNRS researcher affiliated with the Laboratoire de Linguistique Formelle at Université Paris Cité. Her main research interest is the morphosyntax of negation, which she approaches from a typological and nanosyntactic perspective. She is the author of The Morphosyntax of Negative Markers (Mouton de Gruyter, 2020), and co-editor of Exploring Nanosyntax (OUP, 2018) and Adverbial Resumption in Verb Second Languages (OUP, 2023).

Guido Vanden Wyngaerd is a full-time professor at KU Leuven for the fields of Dutch and General Linguistics. His current research focuses on negation and adjectival degrees from a nanosyntactic perspective. His publications include Dissolving Binding Theory (with Johan Rooryck; OUP, 2011), as well as a number of articles in leading international journals and reference works. He is an Associate Editor of Glossa.


Summary

This book presents the latest research in the Nanosyntax framework, a late-insertion theory based on the idea that the elementary building blocks of syntactic trees are limited to single features. The chapters contribute to a better understanding of the framework as a whole and specifically of the lexicalization algorithm.

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