Part-of-Speech Tagging for Historical English
Table 4: Accuracy results for adapting from the PTB to the PPCMBE and the PPCEME of historical English. ∗Error reduction for the normalized PPCEME is computed against the unnormalized SVM accuracy, showing total error reduction.
['Target', 'Normalized', 'baseline SVM', 'baseline MEMM (Stanford)', 'SCL', 'Brown', 'word2vec', 'Fema single embedding', 'Fema attribute embeddings (error reduction)']
Nonetheless, domain adaptation can help: Fema improves performance by 1.3% on the PPCMBE data, and by 3.8% on the unnormalized PPCEME data. Spelling normalization also helps, improving the baseline systems by more than 2.5%. The combination of spelling normalization and domain adaptation gives an overall improvement in accuracy from 74.2% to 79.1%. The relative error reduction is lower than in the temporal adaptation setting: only 19% at best, versus 30% error reduction in temporal adaptation. This is because there are now at least two sources of error — language change and tagset mismatch — and unsupervised domain adaptation cannot address mismatches in the tag set.