From POS tagging to dependency parsing for biomedical event extraction
Table 3: POS tagging accuracies on the test set with gold tokenization. [⋆] denotes a result with a pre-trained POS tagger. We do not provide accuracy results of the pre-trained POS taggers on CRAFT because CRAFT uses an extended PTB POS tag set (i.e. there are POS tags in CRAFT that are not defined in the original PTB POS tag set). Corpus-level accuracy differences of at least 0.17% in GENIA and 0.26% in CRAFT between two POS tagging models are significant at p≤0.05. Here, we compute sentence-level accuracies, then use paired t-test to measure the significance level.
['[BOLD] Model', '[BOLD] GENIA', '[BOLD] CRAFT']
(about 38K sentences) of English PTB WSJ text; given the use of newswire training data, it is unsurprising that this model produces lower accuracy than the retrained tagging models. our 85% training set). It does not support a (re)-training process.