Natural language processing neural network for analogical inference

In this paper, we propose a novel neural network which can learn knowledge from natural language documents and can perform analogy. The conventional neural networks can use only the information the networks learned: knowledge acquisition has been a serious problem. The proposed network solves it by using a large scale dictionary named Google N-gram. In the preprocessing, natural language documents are analyzed by a Japanese dependency structure analyzer named Cabocha. The results are used in the network connection learning. In the analogy process, firing patterns of neurons are memorized in memory parts. When a similar firing pattern is appeared, a memorized pattern is retrieved. This process enables analogical inference. Three kinds of experiments were carried out using goo encyclopedia and Wikipedia as knowledge source. Superior performance of the proposed neural network has been confirmed.