Part-of-Speech Tagging for Historical English
Table 3: Accuracy results for temporal adaptation in the PPCMBE and the PPCEME of historical English. Percentage error reduction is shown for the best-performing method, Fema-attribute.
['Task', 'baseline SVM', 'baseline MEMM (Stanford)', 'SCL', 'Brown', 'word2vec', 'Fema single embedding', 'Fema attribute embeddings (error reduction)']
English spelling had become mostly uniform and stable since around 1700 Among the two baseline systems, MEMM performs slightly better than SVM, showing a small benefit to structured prediction. Among the domain adaptation algorithms, Fema clearly outperforms SCL, Brown clustering and word2vec, with an averaged increase of about 0.5% and 1.5% accuracies on the PPCMBE and the PPCEME test sets respectively. The metadata attribute information boosts performance by a small but consistent margin, 0.1-0.2% on average.