HEGCN
Hierarchical Entity Graph Convolutional Network
**HEGCN**, or **Hierarchical Entity Graph Convolutional Network** is a model for multi-hop relation extraction across documents. Documents in a document chain are encoded using a bi-directional long short-term memory ([BiLSTM](https://paperswithcode.com/method/bilstm)) layer. On top of the BiLSTM layer, two graph convolutional networks ([GCN](https://paperswithcode.com/method/gcn)) are used, one after another in a hierarchy. 

In the first level of the GCN hierarchy, a separate entity mention graph is constructed on each document of the chain using all the entities mentioned in that document. Each mention of an entity in a document is considered as a separate node in the graph. A graph convolutional network (GCN) is used to represent the entity mention graph of each document to capture the relations among the entity mentions in the document. A unified entity-level graph is then constructed across all the documents in the chain. Each node of this entity-level graph represents a unique entity in the document chain. Each common entity between two documents in the chain is represented by a single node in the graph. A GCN is used to represent this entity-level graph to capture the relations among the entities across the documents. 

The representations of the nodes of the subject entity and object entity are concatenated and passed to a feed-forward layer with [softmax](https://paperswithcode.com/method/softmax) for relation classification.
A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents
https://paperswithcode.com/paper/a-hierarchical-entity-graph-convolutional
A Hierarchical Entity Graph Convolutional Network for Relation Extraction across Documents