Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction

Relation Extraction (RE) is a premier task of information extraction (IE) and crucial to many applications including knowledge graph completion (KGC). In recent years, some RE models have employed the topic knowledge of relations through topic words to enrich relation representations, demonstrating better performance than traditional distantly supervised paradigms. However, these models have not taken different syntactic information of relations into account, which have been proven significant in many NLP tasks. In this paper, we propose a novel RE pipeline which incorporates syntactic information into relation representations to enhance RE performance. Representations of sentence and relation in our pipeline are generated by a modified multi-head self-attention structure respectively, where the sentence is represented based on its words and the relation is represented based on the relation-specific embeddings of its topic words. Furthermore, all sentences labeled with the input relation are used to construct an entire weighted directed graph based on their dependency trees. Then, the relation-specific embeddings of words (nodes) in the graph are learned by a GCN-based model. Our extensive experiments have justified that our pipeline significantly outperforms other RE models thanks to the incorporation of syntactic information.