Abstract
We describe our original automata-theoretic, highly mathematical perspective on language representation and learning, and compare with some current research advances and some that are proposed. We find benefits of a mathematical approach to "real life" language learning by humans and today's machines, and review work of researchers who adapt such computational techniques to complex system development and language-related machine processes. Examples include design of communicating agents and proposed development of the Semantic Web. While emphasizing the success of a mathematical perspective on language representation and learning, we also note the danger of assuming that "perfect" results will always be produced by a mathematical approach.
Publisher
Association for Computing Machinery (ACM)