From POS tagging to dependency parsing for biomedical event extraction
Table 8: LAS by POS tag of the dependent.
['Type', '[BOLD] GENIA Biaffine', '[BOLD] GENIA jPTDP', '[BOLD] GENIA NLP4J', '[BOLD] GENIA NNdep', '[BOLD] CRAFT Biaffine', '[BOLD] CRAFT jPTDP', '[BOLD] CRAFT NLP4J', '[BOLD] CRAFT NNdep']
Stanford-Biaffine achieves the highest scores on all these tags except TO where the traditional feature-based model NLP4J-dep obtains the highest score Among listed tags VBG is the least and second least frequent one in GENIA and CRAFT, respectively, and generally associates to longer dependency distances. So, it is reasonable that the lowest scores we obtain on both corpora are accounted for by VBG. The coordinating conjunction tag CC also often corresponds to long dependencies, thus resulting in biggest ranges across parsers on both GENIA and CRAFT. On the remaining POS tags, we generally find similar patterns across parsers and corpora, except for IN and VB where parsers produce 8+% higher scores for IN on GENIA than on CRAFT, and vice versa producing 9+% lower scores for VB on GENIA. Regarding VB, on CRAFT it usually associates to a short dependency distance of 1 word (i.e. head and dependent words are next to each other) with a rate at 80%, and to a distance of 2 words at 15%, while on GENIA it associates with longer dependency distances with a rate at 17% for the distance of 1 word, 31% for the distance of 2 words and 34% for a distance of >5 words. So, parsers obtain much higher scores for VB on CRAFT than on GENIA.