Natural language processing neural network for recall and inference

Tsukasa Sagara, Masafumi Hagiwara

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

1 Citation (Scopus)


In this paper, we propose a novel neural network which can learn knowledge from natural language documents and can perform recall and inference. The proposed network has a sentence layer, a knowledge layer, ten kinds of deep case layers and a dictionary layer. In the network learning step, connections are updated based on Hebb's learning rule. The proposed network can handle a complicated sentence by incorporating the deep case layers and get unlearned knowledge from the dictionary layer. In the dictionary layer, Goi-Taikei, containing 400,000 words dictionary, is employed. Two kinds of experiments were carried out by using goo encyclopedia and Wikipedia as knowledge sources. Superior performance of the proposed neural network has been confirmed.

Original languageEnglish
Title of host publicationArtificial Neural Networks, ICANN 2010 - 20th International Conference, Proceedings
Number of pages4
EditionPART 3
Publication statusPublished - 2010 Nov 8
Event20th International Conference on Artificial Neural Networks, ICANN 2010 - Thessaloniki, Greece
Duration: 2010 Sept 152010 Sept 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6354 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other20th International Conference on Artificial Neural Networks, ICANN 2010

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


Dive into the research topics of 'Natural language processing neural network for recall and inference'. Together they form a unique fingerprint.

Cite this