Collective Entity Disambiguation with Structured Gradient Tree Boosting
Table 3: Cross-domain evaluation: Bag-of-Title (BoT) F1 results on ED datasets. The best results are in bold.
['System', 'AQUAINT', 'MSNBC', 'ACE', 'CWEB', 'WIKI']
SGTB-BS and SGTB-BSG perform quite competitively compared with SGTB-BiBSG. In a cross-domain evaluation setting, the test data is drawn from a different distribution as the training data. Therefore, less expressive models may be preferred as they may learn more abstract representations that will generalize better to out-of-domain data. Nevertheless, our SGTB models achieve better performance than best published results on three of the five popular ED datasets. Specifically, SGTB-BS outperforms the prior SOTA system by absolute 4% F1 on the CWEB dataset, and SGTB-BiBSG performs consistently well across different datasets.