Learning with Noise: Enhance Distantly Supervised Relation Extraction with Dynamic Transition Matrix
Table 1: Comparison with feature-based methods. P@R_10/20/30 refers to the precision when recall equals 10%, 20% and 30%.
['[BOLD] Method', '[BOLD] P@R_10', '[BOLD] P@R_20', '[BOLD] P@R_30']
We also include the comparison with three feature-based methods: Mintz Mintz et al. This indicates that, while human-designed featurs can effectively capture certain relation patterns, their coverage is relatively low. On the other hand, neural network models have more stable performance across different recalls, and att_TM performs generally better than other models, indicating again the effectiveness of our transition matrix method.