# A Part-of-Speech Tagger for Yiddish

arXiv: https://arxiv.org/abs/2204.01175 · HTML: https://mlsanity.com/paper/2204.01175/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 62 references that are in the map.
- First posted on arXiv 3 Apr 2022.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [AllenNLP: A Deep Semantic Natural Language Processing Platform](https://mlsanity.com/paper/1803.07640/) (2018)
- [End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF](https://mlsanity.com/paper/1603.01354/) (2016)
- [Learning Word Vectors for 157 Languages](https://mlsanity.com/paper/1802.06893/) (2018)
- [Penn-Helsinki Parsed Corpus of Early Modern English: First Parsing Results and Analysis](https://mlsanity.com/paper/2112.08532/) (2021)

## Built on by (most cited first, 1 of 1)

- [MameLoshnLM: Yiddish Language Model and Evaluation Benchmark](https://mlsanity.com/paper/2608.05850/) (2026)

## Abstract (arXiv)

We describe the construction and evaluation of a part-of-speech tagger for Yiddish. 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) and 650 million words of OCR'd Yiddish text from the Yiddish Book Center (YBC). Yiddish orthography in the YBC corpus has many spelling inconsistencies, and we present some evidence that even simple non-contextualized embeddings trained on YBC are able to capture the relationships among spelling variants without the need to first "standardize" the corpus. We also use YBC for continued pretraining of contexualized embeddings, which are then integrated into a tagger model trained and evaluated on the PPCHY. We evaluate the tagger performance on a 10-fold cross-validation split, showing that the use of the YBC text for the contextualized embeddings improves tagger performance. We conclude by discussing some next steps, including the need for additional annotated training and test data.