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

Reliable Part-of-Speech Tagging of Historical Corpora through Set-Valued Prediction