Learning, agents, and formal languages: Linguistic applications of interdisciplinary fields

Abstract

This paper focuses on three areas: machine learning, agent technologies and formal language theory. Our goal is to show how the interrelation among agents, learning and formal languages can contribute to the solution of a challenging problem: the explanation of how natural language is acquired and processed. Linguistic contributions of the intersection between machine learning and formal language theory –through the field of grammatical inference– are reviewed. Agent-based formal language models as colonies, grammar systems and eco-grammar systems have been applied to different natural language issues. We review the most relevant applications of these models.