Learning Language Using a Pattern Recognition Approach

IBM Palo Alto Scientific Center, 2530 Page Mill Road, Palo Alto, CA 94303 Abstract A pattern recognition algorithm is described that learns a transition net grammar from positive examples. Two sets of examples-one in English and one in Chinese-are presented. It is hoped that language learning will reduce the knowledge acquisition effort for expert systems and make the natural language interface to database systems more transportable. The algorithm presented makes a step in that direction by providing a robust parser and reducing special interaction for introduction of new words and terms. We are developing a natural language interface to an expert system for message processing.