POS Tagging for Historical Texts with Sparse Training Data
نویسنده
چکیده
This paper presents a method for part-ofspeech tagging of historical data and evaluates it on texts from different corpora of historical German (15th–18th century). Spelling normalization is used to preprocess the texts before applying a POS tagger trained on modern German corpora. Using only 250 manually normalized tokens as training data, the tagging accuracy of a manuscript from the 15th century can be raised from 28.65% to 74.89%.
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تاریخ انتشار 2013