Improving Entity Linking by Modeling Latent Entity Type Information
Table 1: F1 scores on AIDA-B (test set).
['Methods', 'AIDA-B']
The models are divided into two groups: local models and local & global models. As we can see, our proposed model, BERT-Entity-Sim, outperforms all previous methods. Equipped with the global modeling method of \citeauthorganea2017deep \shortciteganea2017deep, the performance of our model further increase to 93.54 with an average 1.32 improvement in terms of F1 over \citeauthorganea2017deep \shortciteganea2017deep. In addition, our method outperforms \citeauthorle-titov-2018-improving \shortcitele-titov-2018-improving model by 0.47 point. The model of \citeauthorle-titov-2018-improving \shortcitele-titov-2018-improving is a multi-relational extension of \citeauthorganea2017deep \shortciteganea2017deep’s global modeling method while keeps exactly the same local context model. Our better local context model should be orthogonal with them and has potential more applications on short texts (e.g. tweets) where global modeling has little benefits. The reason is that BERT-based context representation space and \citeauthorganea2017deep’s entity embeddings space are heterogeneous. \citeauthorganea2017deep ’s entity embeddings are bootstrapped from word embeddings which mainly capture topic level entity relatedness, while BERT-based context representation is derived from BERT which naturally captures type information. The non-parallel information in both context and entity sides makes it difficult to learn the alignment parameter W and results in the poor generalization performance. 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.