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# Diachronic proximity vs. data sparsity in cross-lingual parser projection. A case study on Germanic

**Authors:** [Maria Sukhareva](https://md.mova.elamur.ai/free/en/docs/papers/authors/maria-sukhareva/), [Christian Chiarcos](https://md.mova.elamur.ai/free/en/docs/papers/authors/christian-chiarcos/) · **Year:** 2014 · **Venue:** Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects **Link:** [aclanthology.org/W14-5302](https://aclanthology.org/W14-5302/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) (ACL Anthology, before 2016) — summary only; nothing copied into a release. The Bible corpora are not released under a free licence: evaluation only.

[**7** citations](https://openalex.org/works?filter=cites:W2252059791) openalex · 2026-10-18[**5** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/sukhareva-2014-diachronic-proximity-vs-data-sparsity/) of the 140673 papers whose bibliographies we read[Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects](https://aclanthology.org/volumes/W14-53/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/W14-5302/)
- [PDF](https://aclanthology.org/W14-5302.pdf)
- [DOI 10.3115/v1/w14-5302](https://doi.org/10.3115/v1/w14-5302)
- [OpenAlex](https://openalex.org/W2252059791)

**Authors (2)**

1. [Maria Sukhareva](https://openalex.org/A5040300223) [ORCID](https://orcid.org/0009-0000-1952-8727) · h 7 · 164 citations · 23 works
2. [Christian Chiarcos](https://openalex.org/A5070093758) [ORCID](https://orcid.org/0000-0002-4428-029X) · h 22 · 1690 citations · 326 works

Citations and author metrics from openalex (2026-10-18); authors with the same name are not always told apart.

## What it is about

The question is whether a dependency parser for a historical Germanic variety with little training data can be helped by data from related varieties. Modern English parse trees are projected onto parallel Bible verses in Old English, Middle English, Early Modern High German and Middle Icelandic, using word alignment. Each target word gets a hyperlemma (its aligned English word). A fragment-aware parser is trained on 437 projected sentences per language and tested on 174. The baseline UAS, from target data alone, is .60 for Middle English, .31 for Old English, .41 for German and .32 for Icelandic. Adding data from related varieties helps only German, with a significant effect; Icelandic gets significantly worse when Western Germanic data is added. The authors suggest that varieties separated by about 750 years or less can help each other, while those separated by more than 1,000 years cannot.

## Ideas

- I1. [12. How Mova reads this English today: pipeline, fixes, failures](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/12-mova-pipeline/): project the Mova English parse onto aligned KJV and Wycliffe verses, then train the perceptron on a Middle English set plus the Early Modern set and measure UAS on a held-out gospel. Run twice, and report the per-pair gain with a significance test, as the paper does.
- I2. Use the closed table in `en/src/emode.rs` as the hyperlemma: map `thee`, `hath`, `shalt` to their modern lemmas before parsing, and measure whether the parser gains on archaic verses.
- I3. Negative control: shuffle the alignment between source and target verses. A real projection effect must disappear under this shuffle.

## For Mova

The chapters are [1. The period, the register and the texts Mova reads](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/01-period-and-texts/) (which periods and texts count as related) and [9. The subjunctive, conditions and wishes](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/09-subjunctive/) and `08-word-order.md` (where EModE syntax departs from modern English). The code is `en/src/parse.rs`, `en/src/emode.rs` and `tools/gutenberg`. The measure is UAS on a held-out gospel, against the target-only baseline, with the shuffled-alignment control. Old English is not yet a Mova chapter; the paper's table is a reason to add it.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/v1/w14-5302](https://doi.org/10.3115/v1/w14-5302)

```
@inproceedings{sukhareva2014diachronic,
  title = {{Diachronic proximity vs. data sparsity in cross-lingual parser projection. A case study on Germanic}},
  author = {Sukhareva, Maria and Chiarcos, Christian},
  booktitle = {Proceedings of the First Workshop on Applying NLP Tools to Similar Languages, Varieties and Dialects},
  year = {2014},
  doi = {10.3115/v1/w14-5302},
  url = {https://doi.org/10.3115/v1/w14-5302}
}
```

- [← previous Conundrums in Noun Phrase Coreference Resolution: Making Sense of the State-of-the-Art](https://md.mova.elamur.ai/free/en/docs/papers/stoyanov-2009-conundrums-noun-phrase-coreference-resolution/)
- [next → The role of Alternatives in the interpretation of scalars and numbers: Insights from the inference task](https://md.mova.elamur.ai/free/en/docs/papers/sun-2022-role-alternatives-interpretation-scalars-numbers/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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