  Syntactic annotation of corpora in the form of part-of-speech (POS) tags is a
key requirement for both linguistic research and subsequent automated natural
language processing (NLP) tasks. This problem is commonly tackled using machine
learning methods, i.e., by training a POS tagger on a sufficiently large corpus
of labeled data. While the problem of POS tagging can essentially be considered
as solved for modern languages, historical corpora turn out to be much more
difficult, especially due to the lack of native speakers and sparsity of
training data. Moreover, most texts have no sentences as we know them today,
nor a common orthography. These irregularities render the task of automated POS
tagging more difficult and error-prone. Under these circumstances, instead of
forcing the POS tagger to predict and commit to a single tag, it should be
enabled to express its uncertainty. In this paper, we consider POS tagging
within the framework of set-valued prediction, which allows the POS tagger to
express its uncertainty via predicting a set of candidate POS tags instead of
guessing a single one. The goal is to guarantee a high confidence that the
correct POS tag is included while keeping the number of candidates small. In
our experimental study, we find that extending state-of-the-art POS taggers to
set-valued prediction yields more precise and robust taggings, especially for
unknown words, i.e., words not occurring in the training data.
