Adaptive language acquisition using incremental learning

An incremental approach to solving an algebraic formulation of the language acquisition problem is presented. This problem consists of solving a system of linear equations, where each equation represents a sentence/action pair and each variable denotes a word/action association. The algebraic model for language acquisition has been shown to provide advantages over the relative frequency estimate models when dealing with small-sample statistics. Two incremental methods are investigated to solve the system of linear equations. The incremental methods provide a regularized solution that is shown experimentally to be advantageous over the pseudo-inverse solution for classifying test data. In addition, the methods are more efficient with respect to computational and memory requirements.<>