TY - JOUR
T1 - Solving analogical equations between strings of symbols using neural networks
AU - Kaveeta, Vivatchai
AU - Lepage, Yves
N1 - Funding Information:
This work was supported by a JSPS Grant, Number 15K00317 (Kakenhi C), entitled Language productivity: efficient extraction of productive analogical clusters and their evaluation using statistical machine translation.
Publisher Copyright:
Copyright © 2016 for this paper by its authors.
Copyright:
Copyright 2017 Elsevier B.V., All rights reserved.
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Neural networks
KW - Proportional analogy
UR - http://www.scopus.com/inward/record.url?scp=85017362541&partnerID=8YFLogxK
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M3 - Conference article
AN - SCOPUS:85017362541
VL - 1815
SP - 67
EP - 76
JO - CEUR Workshop Proceedings
JF - CEUR Workshop Proceedings
SN - 1613-0073
T2 - 24th International Conference on Case-Based Reasoning Workshops, ICCBR-WS 2016
Y2 - 31 October 2016 through 2 November 2016
ER -