Part-of-Speech Tagging for Historical English
Table 5: Tagging accuracies of adaptation of our baseline SVM tagger from the PTB to the PPCEME in ablation experiments.
['Feature set', 'IV', 'OOV', 'All']
Word context features are important for obtaining good accuracies on both IV and OOV tokens. Affix features, particularly suffix features, are crucial for the OOV tokens. The orthographic features are shown to be nearly irrelevant, as long as affix features are present. Overall, the high percentage of OOV tokens can be a major source of errors, as the tagging accuracy on OOV tokens is below 50% in our best baseline system. Note that these results are for a classification-based tagger; while the Viterbi-based MEMM tagger performs only marginally better overall (∼0.2% improvement), it is possible that its error distribution might be different due to the advantages of structured prediction.