Learning Dynamic Context Augmentation for Global Entity Linking
Table 4: Ablation Study on Neighbor Entities. We compare the performance of DCA with or without neighbor entities (i.e., 2-hop vs. 1-hop).
['[BOLD] System', '[BOLD] In-KB acc. (%)  [BOLD] SL', '[BOLD] In-KB acc. (%)  [BOLD] RL']
2. Effect of neighbor entities. In contrast to traditional global models, we include both previously linked entities and their close neighbors for global signal. We observe that incorporating these neighbor entities (2-hop) significantly improve the performance (compared to 1-hop) by introducing more related information. And our analysis shows that on average 0.72% and 3.56% relative improvement of 2-hop DCA-(SL/RL) over 1-hop DCA-(SL/RL) or baseline-SL (without DCA) is statistically significant (with P-value < 0.005). This is consistent with our design of DCA.