Neural Collective Entity Linking Based on Recurrent Random Walk Network Learning
Table 1: Experimental results on the validation set AIDA-A using different trade-off parameters.
['[ITALIC] γλ', '[BOLD] 0.1', '[BOLD] 0.3', '[BOLD] 0.5', '[BOLD] 0.7', '[BOLD] 0.9']
To investigate the effect of the trade-off parameter λ and γ, we kept K=5 and then tried different combinations of these two parameters with λ from 0.1 to 0.9 and γ from 0.1 to 0.9. We observed that our method achieves the best performance when the trade-off parameters λ and γ are set to 0.5 and 0.1 respectively. Consequently, all the following experiments only consider the proposed model with λ=0.5 and γ=0.1.