A natural language processing neural network comprehending English

Yuanzhi Ke, Masafumi Hagiwara

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publication2015 International Joint Conference on Neural Networks, IJCNN 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781479919604, 9781479919604, 9781479919604, 9781479919604
Publication statusPublished - 2015 Sept 28
EventInternational Joint Conference on Neural Networks, IJCNN 2015 - Killarney, Ireland
Duration: 2015 Jul 122015 Jul 17

Publication series

NameProceedings of the International Joint Conference on Neural Networks


OtherInternational Joint Conference on Neural Networks, IJCNN 2015


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ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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