  Texts obtained from web are noisy and do not necessarily follow the
orthographic sentence and word boundary rules. Thus, sentence segmentation and
word tokenization systems that have been developed on well-formed texts might
not perform so well on unedited web texts. In this paper, we first describe the
manual annotation of sentence boundaries of an Estonian web dataset and then
present the evaluation results of three existing sentence segmentation and word
tokenization systems on this corpus: EstNLTK, Stanza and UDPipe. While EstNLTK
obtains the highest performance compared to other systems on sentence
segmentation on this dataset, the sentence segmentation performance of Stanza
and UDPipe remains well below the results obtained on the more well-formed
Estonian UD test set.
