A Hidden Knowledge Discovering Approach for Past Tense and Plural Problems to Language Cognition
One purpose of using computational models in cognitive related researches is to use them as a tool to understand human's learning processes, especially focusing on understanding the processes of language development. Although connectionist computational models can simulate the learning process in a black-box way, understanding the inside rational relations is as important as the correlations between input and output. Therefore, this problem can be transferred to a machine learning problem: to discover the hidden knowledge(rules) for a non-classification problem domain. Unfortunately, most available rule extraction techniques are focusing on classification problems, few of them can solve non-classification problems. In this paper, we proposed a hidden knowledge exploration model for the Auto-encoder based computational models which implements the following two functions: (1) simulate the language learning process, (2) reveal the correlations between prior knowledge and the new knowledge through re-building the inherent rules. Experiments prove its efficiency by comparing its fidelity performances with the well performed black-box learning technique(Multi Layer Perceptron Artificial Neural Network).