Perspectives on morphological organization data and analyses / / edited by Ferenc Kiefer, James P. Blevins, Huba Bartos.

• Superior document: Empirical approaches to linguistic theory, volume 10
• Author: International Morphology Meeting Budapest, Hungary)
• TeilnehmendeR: Kiefer, Ferenc,; Blevins, James P.,; Bartos, Huba,
• Place / Publishing House: Leiden ;, Boston : : Brill,, [2017]; ©2017
• Year of Publication: 2017
• Edition: 1st ed.
• Language: English
• Series: Empirical approaches to linguistic theory ; 10.
• Physical Description: 1 online resource (250 pages) :; illustrations.
• Notes: Selection of papers presented at the 16th International Morphology Meeting held in Budapest, from May 31 through June 3, 2014.
• Other title: Preliminary Material --; Editors’ Introduction /; Polyfunctionality and the Variety of Inflectional Exponence Relations /; Verb Morphology and Conjugation Classes in Dunan (Yonaguni) /; Inflection Classes in Transitional Libyan Arabic (tla) /; Person Agreement on Converbs in Udmurt /; Paradigm Leveling: The Decay of Consonant Alternations in Russian /; The Zipfian Paradigm Cell Filling Problem /; Paradigm Realization and the Lexicon /; The Possible Role of Entropy in Processing Argument Dependencies in Hungarian /; A Functional Theory of Gender Paradigms /; Index.
• Bibliography: Includes bibliographical references and index.
• ISBN: 9004342931
• ISSN: 2210-6243
• Hierarchical level: Monograph
• Statement of Responsibility: edited by Ferenc Kiefer, James P. Blevins, Huba Bartos.

This volume contains a selection of recent theoretical studies, deriving from presentations at the 16th International Morphology Meeting (Budapest, 2014), on the organization of morphological paradigms, paradigm complexity, and the inflectional marking of morphosyntactic relations, as well as on the application of information theory to the analysis of morphological systems aiming to achieve a clearer understanding of the close relation between notions of ‘morphological information’ based on ‘uncertainty’ and ‘uncertainty reduction’ and the error-driven structure of discriminative learning models.