In morphologically complex languages, many high-level tasks in natural language
processing rely on accurate morphosyntactic analyses of the input. However, in
light of the risk of error propagation in present-day pipeline architectures for basic
linguistic pre-processing, the state of the art for morphosyntactic tagging is still
not satisfactory. The main obstacle here is data sparsity inherent to natural lan-
guage in general and highly inflected languages in particular.
In this work, we investigate whether semi-supervised systems may alleviate the
data sparsity problem. Our approach uses word clusters obtained from large
amounts of unlabelled text in an unsupervised manner in order to provide a su-
pervised probabilistic tagger with morphologically informed features. Our evalua-
tions on a number of datasets for the Polish language suggest that this simple
technique improves tagging accuracy, especially with regard to out-of-vocabulary
words. This may prove useful to increase cross-domain performance of taggers,
and to alleviate the dependency on large amounts of supervised training data,
which is especially important from the perspective of less-resourced languages
