Feature-Extracting Functions for Neural Logic Rule Learning
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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 for visual sentiment analysis. We combine the declarative first-order logic rules which represent the human knowledge in a logically-structured format making use of feature-extracting functions. 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. In contrast with other neural logic approaches, the programmatic nature in practice of these functions do not require any kind of special mathematical encoding, which makes our method very general in nature. 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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