Solving analogical equations between strings of symbols using neural networks

Vivatchai Kaveeta, Yves Lepage

Research output: Contribution to journalConference articlepeer-review

5 Citations (Scopus)


A neural network model to solve analogical equations between strings of symbols is proposed. The method transforms the input strings into two fixed size alignment matrices. The matrices act as the input of the neural network which predicts two output matrices. Finally, a string decoder transforms the predicted matrices into the final string output. By design, the neural network is constrained by several properties of analogy. The experimental results show a fast learning rate with a high prediction accuracy that can beat a baseline algorithm.

Original languageEnglish
Pages (from-to)67-76
Number of pages10
JournalCEUR Workshop Proceedings
Publication statusPublished - 2016
Event24th International Conference on Case-Based Reasoning Workshops, ICCBR-WS 2016 - Atlanta, United States
Duration: 2016 Oct 312016 Nov 2


  • Neural networks
  • Proportional analogy

ASJC Scopus subject areas

  • General Computer Science


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