A natural language processing neural network comprehending English

In this paper, a natural language neural network model based on the analysis of the structure of sentences is proposed. The proposed neural network consists of 5 layers: sentence-layer, clause-layer, phrase-layer, word-layer, and concept-layer. The input text is split into different levels as sentences, clauses, phrases and words. Then neurons are allocated for each sentence, clause, phrase and word in the corresponding layers. The neurons in each of the upper 4 layers are connected to the other neurons in the adjacent layers according to the breakdown structure of each sentence in the input text. Concept-layer contains neurons of synsets. Each neuron of a synset is connected to its hypernyms, hyponyms and holonyms. Each neuron in the word-layer is connected to the neuron of its corresponding synset. Energy propagation is used to train the neural network and recall. Experiments to evaluate the association ability and the noise tolerance are performed. The results show that the proposed neural network has a fairly splendid recall ability and noise tolerance. This neural network is also applied to answer some TOEIC test questions in the reading comprehension part and achieved scores equivalent to the average level of human examinees, which shows its ability of learning knowledge in the test passages. The proposed neural network supports a novel way for artificial intelligence to flexibly learn and recall knowledge in English.