We describe the construction and evaluation of a part-of-speech tagger for
Yiddish (the first one, to the best of our knowledge). This is the first step
in a larger project of automatically assigning part-of-speech tags and
syntactic structure to Yiddish text for purposes of linguistic research. We
combine two resources for the current work - an 80K word subset of the Penn
Parsed Corpus of Historical Yiddish (PPCHY) (Santorini, 2021) and 650 million
words of OCR'd Yiddish text from the Yiddish Book Center (YBC). We compute word
embeddings on the YBC corpus, and these embeddings are used with a tagger model
trained and evaluated on the PPCHY. Yiddish orthography in the YBC corpus has
many spelling inconsistencies, and we present some evidence that even simple
non-contextualized embeddings are able to capture the relationships among
spelling variants without the need to first "standardize" the corpus. We
evaluate the tagger performance on a 10-fold cross-validation split, with and
without the embeddings, showing that the embeddings improve tagger performance.
However, a great deal of work remains to be done, and we conclude by discussing
some next steps, including the need for additional annotated training and test
data