Natural language generation using automatically constructed lexical resources

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

8 Citations (Scopus)

Abstract

One of the practical targets of neural network research is to enable conversation ability with humans. This paper proposes a novel natural language generation method using automatically constructed lexical resources. In the proposed method, two lexical resources are employed: Kyoto University's case frame data and Google N-gram data. Word frequency in case frame can be regarded to be obtained by Hebb's learning rule. The co-occurence frequency of Google N-gram can be considered to be gained by an associative memory. The proposed method uses words as an input. It generates a sentence from case frames, using Google N-gram as to consider co-occurrence frequency between words. We only use lexical resources which are constructed automatically. Therefore the proposed method has high coverage compared to the other methods using manually constructed templates. We carried out experiments to examine the quality of generated sentences and obtained satisfactory results.

Original languageEnglish
Title of host publication2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program
Pages980-987
Number of pages8
DOIs
Publication statusPublished - 2011 Oct 24
Event2011 International Joint Conference on Neural Network, IJCNN 2011 - San Jose, CA, United States
Duration: 2011 Jul 312011 Aug 5

Publication series

NameProceedings of the International Joint Conference on Neural Networks

Other

Other2011 International Joint Conference on Neural Network, IJCNN 2011
CountryUnited States
CitySan Jose, CA
Period11/7/3111/8/5

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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  • Cite this

    Ito, N., & Hagiwara, M. (2011). Natural language generation using automatically constructed lexical resources. In 2011 International Joint Conference on Neural Networks, IJCNN 2011 - Final Program (pp. 980-987). [6033329] (Proceedings of the International Joint Conference on Neural Networks). https://doi.org/10.1109/IJCNN.2011.6033329