Knowledge Extraction and Recurrent Neural Networks: An Analysis of an Elman Network trained on a Natural Language Learning Task

We present results of experiments with Elman recurrent neural networks (Elman, 1990) trained on a natural language processing task. The task was to learn sequences of word categories in a text derived from a primary school reader. The grammar induced by the network was made explicit by cluster analysis which revealed both the representations formed during learning and enabled the construction of state-transition diagrams representing the grammar. A network initialised with weights based on a prior knowledge of the text's statistics, learned slightly faster than the original network. In this paper we focus on the extraction of grammatical rules from trained Artificial Neural Networks and, in particular, Elman-type recurrent networks (Elman, 1990). Unlike Giles & Omlin (1993 a,b) who used an ANN to simulate a deterministic Finite State Automaton (FSA) representing a regular grammar, we have extracted FSA's from a network trained on a natural language corpus. The output of k-means cluster analysis is converted to state-transition diagrams which represent the grammar learned by the network. We analyse the prediction and generalisation performance of the grammar.