Applications of Neural Networks to Semantic Understanding

In the context of artificial neural networks, semantics refers to the capabilities of networks to understand and represent the meaning of information, specifically how the meaning of words and sentences are discerned. The advance of neural networks in recent decades has had a profound impact on both neuroscience and philosophy dealing with semantic processing and understanding.

In the brain, semantics are processed by networks of neurons in several regions of the brain. The specific neural underpinnings of semantic understanding is a currently evolving area of research, with a variety of hypotheses on the exact mechanisms for semantic processing in the brain being explored at this time. Nevertheless, the brain’s ability to continually learn and refine its semantic understanding has provided an important model for advancements in artificial systems attempting to emulate these features.

In artificial systems, neural networks analyze linguistic data— identifying patterns to realize context and relationships between such data to create representations of meaning. A variety of model types are popular for attempting to encode semantics, including convolutional neural networks (CNN), recurrent neural networks (RNN), and transformer models.