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) layer. On top of the BiLSTM layer, two graph convolutional networks (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 for relation classification.