  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 behavior 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.
