ERGM: A multi-stage joint entity and relation extraction with global entity match

Joint entity and relation extraction is a fundamental and important task in the process of building knowledge graphs. At present, many researchers have proposed their own methods to solve this task, but these studies often have some limitations, such as irrelevant relation prediction, and lack of information interaction between the relation and entity. The complex model structure leads to inefficiency and does not make good use of the associations between the various subtasks. We propose a novel lightweight joint extraction model based on a global entity matching strategy. Specifically, the proposed framework contains three components: Relation Extraction Module, Relation Attention Based Entity Recognition Module and Global Entity Pairing Module. The Relation Extraction Module extracts candidate relations in the sentence, and the Relation Attention Based Entity Recognition Module introduces a relation attention mechanism based on the obtained candidate relations to fuse the information of the relations so as to better identify entities in the sentence. Then use entity vector representations to construct an affine transformation-based global entity matching matrix under a specific relation for triple extraction. Our model decomposes entity and relation extraction into three sub-tasks, which greatly simplifies the model structure, and the tasks are interrelated, making full use of the relevant information. In addition, we introduce a negative sampling strategy to alleviate the exposure bias problem of the model. We validate Our model on public dataset, it not only can effectively solve the triple overlap problem but also achieved a significant time performance speedup and effectively reduce memory occupation.(c) 2023 Elsevier B.V. All rights reserved.

Automated summary

Joint entity and relation extraction is a crucial task in knowledge graph construction. Existing methods have limitations such as unrelated relation prediction and lack of interaction between relation and entity. We propose a lightweight joint extraction model based on global entity matching strategy, consisting of three components: Relation Extraction Module, Relation Attention Based Entity Recognition Module, and Global Entity Pairing Module. Our model simplifies the structure and leverages relevant information by decomposing extraction into sub-tasks. We also introduce negative sampling to alleviate exposure bias, achieving effective triple overlap resolution, improved performance, and reduced memory usage.