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Feature-Extracting Functions for Neural Logic Rule Learning

conference contribution
posted on 2020-11-29, 00:00 authored by Shashank GuptaShashank Gupta, Antonio Robles-KellyAntonio Robles-Kelly
In this paper, we present a method aimed at integrating domain knowledge abstracted as logic rules into the predictive behaviour of a neural network using feature extracting functions. We combine the declarative first-order logic rules which represents the human knowledge in a logically-structured format akin to that introduced in [1] with feature-extracting functions which act as the decision rules presented in [2]. These functions are embodied as programming functions which can represent, in a straightforward manner, the applicable domain knowledge as a set of logical instructions and provide a cumulative set of probability distributions of the input data. These distributions can then be used during the training process in a mini-batch strategy. We also illustrate the utility of our method for sentiment analysis and compare our results to those obtained using a number of alternatives elsewhere in the literature.

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9781728191089

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E3 Extract of paper

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2020 Digital Image Computing: Techniques and Applications, DICTA 2020

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