NRE models introduce neural networks to automatically extract semantic features from text, and can effectively capture textual information and generalize to a wider range of data. NRE studies mainly focus on designing and using different network architectures to capture relational semantics within text. NRE differs from SREs by using word embeddings and position embeddings as inputs so that semantic meanings of words can be combined with a specification of the relative distances between words and entities.