Improving Entity Linking by Modeling Latent Entity Type Information
Table 2: F1 scores on five out-domain test sets. Underlined scores denote the corresponding model outperforms the baseline.
['Methods', 'MSNBC', 'AQUAINT', 'ACE2004', 'CWEB', 'WIKI', 'Avg']
On average, our proposed model (BERT-Entity-Sim) outperforms the local & global version of \citeauthorganea2017deep,le-titov-2018-improving \shortciteganea2017deep ,le-titov-2018-improving by an average 0.80 and 0.51 on F1. all the state-of-the-art entity linking models by a large margin, even surpass \citeauthorle-titov-2018-improving \shortcitele-titov-2018-improving by 3.28 F1 points on AIDA-CoNLL test set. This result shows that a better type prediction system can further improve upon the state-of-the-state entity linking systems. However, in the predict setting, the type injection models have worse performance than the baseline. The degradation might be attributed to the poor performance of the two state-of-the-art fine grained entity typing systems. To verify this, we measure the performance of the two typing systems on AIDA-CoNLL development set.