Handling categorical field values in machine learning applications

Disclosed are systems and methods for handling categorical field values in machine learning applications, and particularly neural networks. Categorical field values are generally transformed into vectors prior to being passed to a neural network. However, low-dimensionality vectors limit the ability of the network to understand correlations between contextually, semantically, or characteristically similar values. High-dimensionality vectors, in contrast, can overwhelm neural networks, causing the network to seek correlations with respect to individual dimensional values, which correlations may be illusory. The present disclosure relates to a hierarchical neural network that includes a main network as well as one or more auxiliary networks. Categorical field values are processed in an auxiliary network, to reduce a dimensionality of the value before being processed by the main network. This enables contextual, semantic, and characteristic correlations to be identified without overwhelming the network as a whole.