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.
|ジャーナル||CEUR Workshop Proceedings|
|出版ステータス||Published - 2016|
|イベント||24th International Conference on Case-Based Reasoning Workshops, ICCBR-WS 2016 - Atlanta, United States|
継続期間: 2016 10月 31 → 2016 11月 2
ASJC Scopus subject areas
- コンピュータ サイエンス（全般）