From POS tagging to dependency parsing for biomedical event extraction
Table 5: LAS (F1) scores of Stanford-Biaffine on GENIA, by frequent dependency labels in the left dependencies. “Prop.” denotes the occurrence proportion in each distance bin.
['Type', '< [BOLD]  −5 Prop.', '< [BOLD]  −5 LAS', '−5 Prop.', '−5 LAS', '−4 Prop.', '−4 LAS']
On both corpora, higher scores are also associated with shorter distances. There is one surprising exception: on GENIA, in distance bins of −4, −5 and < −5, Stanford-Biaffine and jPTDP obtain higher scores for longer distances. This may result from the structural characteristics of sentences in the GENIA corpus. We find amod and nn are the two most difficult to predict dependency relations (the same finding applied to jPTDP). They appear much more frequently in the bins −4 and −5 than in bin < −5, explaining the higher overall score for bin < −5.