A Walk-based Model on Entity Graphs for Relation Extraction
This addresses relation extraction for natural language processing, offering a novel graph-based approach that is incremental in improving entity pair modeling.
The paper tackles relation extraction by modeling entity interactions in a sentence as a fully-connected graph with position-aware edges and multi-length walks, achieving performance comparable to state-of-the-art on the ACE 2005 dataset without external tools.
We present a novel graph-based neural network model for relation extraction. Our model treats multiple pairs in a sentence simultaneously and considers interactions among them. All the entities in a sentence are placed as nodes in a fully-connected graph structure. The edges are represented with position-aware contexts around the entity pairs. In order to consider different relation paths between two entities, we construct up to l-length walks between each pair. The resulting walks are merged and iteratively used to update the edge representations into longer walks representations. We show that the model achieves performance comparable to the state-of-the-art systems on the ACE 2005 dataset without using any external tools.