论文标题
神经逻辑规则学习的功能提取功能
Feature Extraction Functions for Neural Logic Rule Learning
论文作者
论文摘要
将象征性的人类知识与神经网络相结合，提供了基于规则的对输出的解释。在本文中，我们提出了提取功能的功能，以将作为逻辑规则的人类知识整合到神经网络的预测行为中。这些功能体现为编程功能，该功能代表适用的域知识作为一组逻辑指令，并在输入数据上提供了独立特征的修改分布。与其他现有的神经逻辑方法不同，这些功能的程序化性质意味着它们不需要任何特殊的数学编码，这使我们的方法本质上非常通用和灵活。我们说明了我们的情感分类方法的表现，并将我们的结果与使用两个基准获得的结果进行了比较。
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.
