An improved neural network model for joint POS tagging and dependency parsing
Table 5: Downstream task scores Precision (Prec.), Recall (Rec.) and F1 for our UniMelb team. The subscript in the F1 column denotes the unofficial rank of UniMelb over 17 participating teams at EPE 2018 (Fares et al., 2018). “SP17” denotes the F1 scores obtained by the EPE 2017 system Stanford-Paris Schuster et al. (2017) with respect to (w.r.t.) the Stanford basic dependencies. The subscript in the SP17 column denotes the F1 scores obtained by Stanford-Paris w.r.t. the UD-v1-enhanced type of dependency representations, in which the average F1 score at 60.51 is the highest one at EPE 2017.
['[BOLD] Task', '[BOLD] Development set Pre.', '[BOLD] Development set Rec.', '[BOLD] Development set F1', '[BOLD] Development set SP17', '[BOLD] Evaluation set Pre.', '[BOLD] Evaluation set Rec.', '[BOLD] Evaluation set F1', '[BOLD] Evaluation set SP17']
Since we employed external training data, our obtained scores are not officially ranked. In total 17 participating teams, we obtained the highest average F1 score over the three downstream tasks (i.e., we ranked first, unofficially). In particular, we achieved the highest F1 scores for both biomedical event extraction and opinion analysis. Our results may be high because the training data we used is larger than the English UD treebanks used by other teams. Both EPE 2017 and 2018 campaigns use the same downstream task setups, therefore the downstream task scores are directly comparable. Note that Stanford-Paris employed the state-of-the-art biaffine dependency parser Dozat et al. In particular, Stanford-Paris not only used the WSJ sections 02-21 and the training split of the GENIA treebank (as we did), but also included the Brown corpus. The downstream application of negation resolution requires parsing of fiction, which is one the genres included in the Brown corpus. Hence it is reasonable that the Stanford-Paris team produced better negation resolution scores than we did.