Fine-Grained Entity Typing for Domain Independent Entity Linking
Table 2: Accuracy on the CoNLL development set (testa) and the CoNLL test set (testb). †: trained with additional large unlabeled data. ‡: uses in-domain unlabeled data (RCV1). Our model outperforms the baselines and models using similar data from prior work.
['Model', 'Dev', 'Test']
Our model outperforms all baselines by a substantial margin. The Most Frequent Entity baseline performs poorly on both development and test set. Interestingly, the simpler CBoW+word2vec model is the strongest baseline here, outperforming the GRU+Attn model in both general and transductive settings. Our model achieves the strongest performance on both the dev and test data. Interestingly, our model also has a much smaller drop from dev to test, only losing 2 points, compared to the transductive models, which drop by 7 points. The CoNLL testb set is slightly “out-of-domain” for the training set with respect to the time period it was drawn from, indicating that our method may have better generalization than more conventional neural models in the transductive setting.