Tensor Product Representations of Subregular Formal Languages

Abstract

This paper provides a geometric characterization of subclasses of the regular languages. We use finite model theory to characterize objects like strings and trees as relational structures. Logical statements meeting certain criteria over these models define subregular classes of languages. The semantics of such statements can be compiled into tensor structures, using multilinear maps as function application for evaluation. This method is applied to consider two properly subregular languages over different string models.

Publication
In *Neural-Symbolic Learning and Reasoning @ IJCAI 2019
Jon Rawski
Jon Rawski
Researcher

I am a PhD student working at the interface of mathematics, linguistics, cognitive science, and algorithmic learning theory.

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