Teachability on Grammatical Inference and its Applications for Natural Language Processing

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In this study, we developed a new learning algorithm for a subclass of context-free languages. The main result during the supported term is that we showed polynomial time teachability of a subclass of simple deterministic languages. This language class is polynomial time teachable but it would be hard to learn from example based queries in polynomial time. Thus, our result shows a difference between teaching and learning via queries. Next, we applied our algorithm to natural language processing, then we developed a text segmentation algorithm for conversations and showed the performance improvement. In addition, we applied our algorithm to game tree search. Then we showed an improvement of reinforcement learning algorithm for making the evaluation function. We also confirmed the learning performance is improved by our algorithm.