A Long-Tail Relation Extraction Model Based on Dependency Path and Relation Graph Embedding

Distant supervision, a method for relation extraction, leverages knowledge base triples to label entities and relations in text, but this leads to noisy labels and long-tail problems. Among long-tail dependency structures, the hierarchy tree of relations is the most classical and has demonstrated great efficacy in information extraction. However, the hierarchical tree of relations presents a challenge in obtaining sufficient information representation in cases where there is no sibling node or parent node without sibling node. To address this challenge, the use of constraint graphs has been proposed, but such approaches neglect the hierarchical information in the relations. To overcome this limitation, we propose a model based on dependency paths and relational graph embeddings. The model utilizes two relational graph structures, the constraint graph and the relation hierarchy tree, for relation learning, with the aim of transferring the knowledge learned in the data-rich relation to the long-tail relation. Additionally, the model leverages the shortest dependency path between entity pairs to increase the discriminative power of entity pairs in different bags for multi-instance learning. Experimental results show that the model achieves an AUC of 54.3% on the NYT-10 dataset and 86.3% on Hit@15 (<100).