SEE: Syntax-aware Entity Embedding for Neural Relation Extraction

Understand

Distant supervised relation extraction is an efficient approach to scale relation extraction to very large corpora, and has been widely used to find novel relational facts from plain text.

• Recent studies on neural relation extraction have shown great progress on this task via modeling the sentences in low-dimensional spaces, but seldom considered syntax information to model the entities.
• In this paper, we propose to learn syntax-aware entity embedding for neural relation extraction.
• First, we encode the context of entities on a dependency tree as sentence-level entity embedding based on tree-GRU.