This book constitutes the proceedings of the 12th Biennial Meeting on Mathematics in Language, MOL 12, held in Nara, Japan, in September 2011.

Presented in this volume are 12 carefully selected papers, as well as the paper of the invited speaker Andreas Maletti. The papers cover such diverse topics as formal languages (string and tree transducers, grammar-independent syntactic structures, probabilistic and weighted context-free grammars, formalization of minimalist syntax), parsing and unification, lexical and compositional semantics, statistical language models, and theories of truth.

Edited in collaboration with FoLLI, the Association of Logic, Language and Information, this book constitutes the 4th volume of the FoLLI LNAI subline; containing the refereed proceedings of the 16h International Workshop on Logic, Language, Information and Computation, WoLLIC 2009, held in Tokyo, Japan, in June 2009.

The 25 revised full papers presented together with six tutorials and invited talks were carefully reviewed and selected from 57 submissions. The papers cover some of the most active areas of research on the frontiers between computation, logic, and linguistics, with particular interest in cross-disciplinary topics. Typical areas of interest are: foundations of computing and programming; novel computation models and paradigms; broad notions of proof and belief; formal methods in software and hardware development; logical approach to natural language and reasoning; logics of programs, actions and resources; foundational aspects of information organization, search, flow, sharing, and protection.

This book investigates the learnability of various classes of classical categorial grammars within the Gold paradigm of identification in the limit from positive data. Learning from structure and learning from flat strings are considered. The class of k-valued grammars, for k = 1,2,3,..., is shown to be learnable both from structures and from strings, while the class of least-valued grammars and the class of least-cardinality grammars are shown to be learnable from structures. In proving these learnable results, crucial use is made of a theorem on the concept known as finite elasticity. The learning algorithms used in this work build on Buszkowski and Penn's algorithm for finding categorial grammars from input consisting of functor-argument structures.

This book investigates the learnability of various classes of classical categorial grammars within the Gold paradigm of identification in the limit from positive data. Learning from structure and learning from flat strings are considered. The class of k-valued grammars, for k = 1,2,3,..., is shown to be learnable both from structures and from strings, while the class of least-valued grammars and the class of least-cardinality grammars are shown to be learnable from structures. In proving these learnable results, crucial use is made of a theorem on the concept known as finite elasticity. The learning algorithms used in this work build on Buszkowski and Penn's algorithm for finding categorial grammars from input consisting of functor-argument structures.