  This research paper presents a part-of-speech (POS) annotated dataset and
tagger tool for the low-resource Uzbek language. The dataset includes 12 tags,
which were used to develop a rule-based POS-tagger tool. The corpus text used
in the annotation process was made sure to be balanced over 20 different fields
in order to ensure its representativeness. Uzbek being an agglutinative
language so the most of the words in an Uzbek sentence are formed by adding
suffixes. This nature of it makes the POS-tagging task difficult to find the
stems of words and the right part-of-speech they belong to. The methodology
proposed in this research is the stemming of the words with an affix/suffix
stripping approach including database of the stem forms of the words in the
Uzbek language. The tagger tool was tested on the annotated dataset and showed
high accuracy in identifying and tagging parts of speech in Uzbek text. This
newly presented dataset and tagger tool can be used for a variety of natural
language processing tasks such as language modeling, machine translation, and
text-to-speech synthesis. The presented dataset is the first of its kind to be
made publicly available for Uzbek, and the POS-tagger tool created can also be
used as a pivot to use as a base for other closely-related Turkic languages.
