S-mart: Novel Tree-based Structured Learning AlgorithmsApplied to Tweet Entity Linking
Table 5: IE-driven evaluation results on the full NEEL Test and TACL-IE datasets for different models. The best results with basic features are in bold. The results are underlined if adding entity-entity features gives the overall best results.
['Model', 'NEEL Test P', 'NEEL Test R', 'NEEL Test F1', 'TACL-IE P', 'TACL-IE R', 'TACL-IE F1']
On the NEEL Test dataset, non-linear models significantly outperform linear models, tree-based models perform much better than alternative methods, and our tree-based structured prediction method S-mart achieves the best results. The tree-based non-structured model MART yields best performance on the TACL-IE dataset with basic features, which slightly wins over S-mart by 0.5% F1. The entity-entity relationships improve the performance of S-mart on the TACL-IE dataset by 3.6 points of F1.