Agent-Based Unsupervised Grammar Induction.
Guy De Pauw · 2005
In this paper, we describe an agent-based evolutionary computing approach to unsupervised grammar induction called grael (Grammar Evolution). Extending a general framework for data driven grammar optimization and induction, the evolutionary setup of grael can be used to automatically induce and optimize grammars from scratch on the basis of unstructured text. Agents are equipped with a very basic grammar induction module to bootstrap structure. Over an extended series of inter-agent interactions, the agents optimize their grammars, as the society attempts to converge towards an optimal grammar. We highlight two proof-of-the-principle experiments that show that grael is able to yield a reasonably performant phrase structuregrammar in an unsupervised manner. In contrast to the current state-of-the-art systems, grael does not require a large amount of data to induce a workable grammar. 1