Joint Extraction of Entities and Relations with a Hierarchical Multi-task Tagging Model
Table 1: Results of different methods on extraction of both entities and their relations. We run the model for 10 times and report the average results as well as the standard deviation.
['Method', 'NYT10  [ITALIC] Prec.', 'NYT10  [ITALIC] Rec.', 'NYT10  [ITALIC] F1', 'NYT11  [ITALIC] Prec.', 'NYT11  [ITALIC] Rec.', 'NYT11  [ITALIC] F1']
Our HMT model substantially outperforms all the baselines in terms of F1-score and achieves 1.6% and 6.1% improvements over the best neural network-based joint extraction method HRL This demonstrates the effectiveness of our proposed model on jointly extracting entities and relations.