Automatic Generation of High Quality CCGbanks for Parser Domain Adaptation
Table 3: Results on the biomedical domain dataset (§5.3). P and R represent precision and recall, respectively. The scores of C&C and EasySRL fine-tuned on the GENIA1000 is included for comparison (excerpted from Lewis et al. (2016)).
['Method', 'P', 'R', 'F1']
The plain depccg already achieves higher scores than these methods, and boosts when combined with ELMo (improvement of 2.73 points in terms of F1). Fine-tuning the parser on GENIA1000 results in a mixed result, with slightly lower scores. This is presumably because the automatically annotated Head First dependencies are not accurate. Finally, by fine-tuning on the Genia CCGbank, we observe another improvement, resulting in the highest 86.52 F1 score.