POS and full morphology tagging

The neural network tagger CLStM has been applied to the Old Russian Žitie Evfimija Velikogo (GIM, Chud. 20), a copy of the second half of the 14th century. The strengths of this tagger consist in its ability to automatically annotate an orthographically non-normalized text with dozens of pages within a few minutes, yielding a high accuracy with respect to part of speech and morphological features. Moreover, the tagger is capable of disambiguating case syncretism to a large extent, even in split constructions. Manual correction of the automatic tagging will result in a correctly tagged text considerably faster than when using a rule-based tagger or tagging completely manually. The weaknesses of the CLStM-tagger comprise certain examples of incorrect POS-tagging, sometimes incomplete or incorrect attribution of morphological categories to some parts of speech. Superscript letters and punctuation can pose special problems, normalization of punctuation will achieve better tagging results. The proportion of correct tags is higher when the token has been seen during the training process; unknown words (OOV) show a higher error rate. In the paper, we analyze the strengths and weaknesses of the tagger by providing specific examples. Furthermore, we demonstrate how to use automatically tagged, uncorrected data for quantitative analysis.