Recursive neural networks learn compositional representations for sentences recursively. Recursive neural networks based on augmented dependency paths can be used for relation extraction in order to integrate syntactic information. 


Recursive neural networks for NRE


CNNs effectively model local textual patterns and can be adopted over shortest dependency paths in order to integrate syntactic information.


Convolutional neural networks for NRE


RNNs can better handle long sequential data and can be adopted over shortest dependency paths in order to integrate syntactic information. Deep RNNs make further use of dependency paths as well. 


Recurrent neural networks for NRE


GNNs that build word/entity graphs for reasoning can be used


Graph neural networks for NRE


Attention-based neural networks for NRE use attention mechanism to aggregate global relational information 


Attention-based neural networks for NRE


- Recursive neural networks
- Convolutional neural networks 
- Recurrent neural networks 
- Graph neural networks 
- Attention-based neural networks


Columbia University

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. 


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