Using random graphs in learning past tenses

This paper presents an automatic learning technique. Its strength is demonstrated by a bench mark problem for evaluating learning systems-learning the past-tenses of English verbs. To deal with training patterns of variable length and to exploit probabilistic properties inherent in the English language, our method uses random graphs. Due to the large amount of redundancy between the base form and the past tense form of most verbs, only the endings of these forms are of interest in the classification process. These are extracted automatically. Taking advantage of the sequential nature of words, the complete graphs can be relaxed to sequences with great reduction in the computational requirements. A significant merit of this method over the connectionist models is that the knowledge acquired is expressed automatically in a rule-based form which simplifies the classification process of unknown verbs