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Feature Extraction Functions for Neural Logic Rule Learning

Combining symbolic human knowledge with neural networks provides a rule-based ante-hoc explanation of the output. In this paper, we propose feature extracting functions for integrating human knowledge abstracted as logic rules into the predictive behaviour of a neural network. These functions are embodied as programming functions, which represent the applicable domain knowledge as a set of logical instructions and provide a modified distribution of independent features on input data. Unlike other existing neural logic approaches, the programmatic nature of these functions implies that they do not require any kind of special mathematical encoding, which makes our method very general and flexible in nature. We illustrate the performance of our approach for sentiment classification and compare our results to those obtained using two baselines.

History

Volume

12644

Pagination

98-107

Location

Online from Padua, Italy

Start date

2021-01-21

End date

2021-01-22

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783030739720

Language

eng

Publication classification

E1 Full written paper - refereed

Editor/Contributor(s)

Torsello A, Rossi L, Pelillo M, Biggio B, Robles-Kelly A

Title of proceedings

S+SSPR 2021 : Proceedings from the Joint IAPR Structural, Syntactic, and Statistical Pattern Recognition International Workshop

Event

Structural, Syntactic, and Statistical Pattern Recognition. Workshop (2021 : Online from Padua, Italy)

Publisher

Springer International Publishing

Place of publication

Cham, Switzerland

Series

Lecture Notes in Computer Science; 12644

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