In this paper, we present a novel end-to-end neural model based on graph convolutional networks (GCN) for jointly extracting entities and relations between them. It divides the joint extraction into two sub-tasks, first detecting entity spans and identifying entity relations type simultaneously. To consider the complete interaction between entities and relations, we propose a novel relation-aware attention mechanism to obtain the relation representation between two entity spans. Therefore, a complete graph is constructed based on all extracted entity spans where the nodes are entity spans and the edges are relation representation. Besides, we improve original GCN to utilize both adjacent node features and edge information when encoding node feature. Experiments are conducted on two public datasets and our model outperforms all baseline methods. INDEX TERMS Graph convolutional network, joint extraction of entities and relations, attention, sequential labelling.
The microstructure and mechanical properties of a medium manganese quenching and partitioning (Q&P) steel for automobile were investigated by optical microscope (OM), scanning electron microscope (SEM), X-ray diffraction (XRD) and mechanical property test. The grain size and recovery degree were greatly affected by annealing temperature normally. The result shows that the medium manganese steel after quenching and partitioning (Q&P) heat treatment exhibited good mechanical properties. The maximum tensile strength and yield strength was 1280MPa and 1421MPa at 600°C, respectively. Additionally, the product of strength and plasticity could reached to 40472MPa×% at 640°C. Annealing temperature also had a great influence on the volume of retained austenite which increases linearly with the rise of annealing temperature as well.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.