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# Part-of-Speech Tagging for Middle English through Alignment and Projection of Parallel Diachronic Texts

**Authors:** Taesun Moon and [Jason Baldridge](https://md.mova.elamur.ai/free/en/docs/papers/authors/jason-baldridge/) · **Year:** 2007 · **Venue:** Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL) **Link:** [aclanthology.org/D07-1041](https://aclanthology.org/D07-1041/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only

[**21** citations](https://openalex.org/works?filter=cites:W70524345) openalex · 2026-10-18[**11** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2007-moon-part-speech-tagging-middle-english/) of the 140673 papers whose bibliographies we read[Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)](https://aclanthology.org/volumes/D07-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/)

- [Publisher's page](https://aclanthology.org/D07-1041/)
- [PDF](https://aclanthology.org/D07-1041.pdf)
- [OpenAlex](https://openalex.org/W70524345)

**Authors (2)**

1. [Taesun Moon](https://openalex.org/A5004719091) h 9 · 214 citations · 21 works
2. [Jason Baldridge](https://openalex.org/A5048931310) [ORCID](https://orcid.org/0000-0002-4712-1841) · h 46 · 8016 citations · 139 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 paper builds a Middle English tagger without hand-tagged data. Modern English text is tagged by a maximum-entropy tagger trained on the Wall Street Journal. The tags are projected to Middle English Wycliffe Bible text through word alignments of parallel verses. A bigram tagger cleans the projected tags, and a second maximum-entropy tagger is trained on the WSJ text and the cleaned Middle English text. On Biblical test text it reaches 84.8% accuracy for coarse tags, against a baseline of 63.4%. Other Middle English texts stay in the 60s. About 600 hand-labelled sentences were needed to match the best bootstrapped line.

## Ideas

- I1. `en/src/ptag.rs` and `tools/treeagree`: project tags from `en` onto a verse-aligned parallel text (for example, a translation of a tools/gutenberg lesson), then train the perceptron on the projected tags. Measure accuracy on held-out lessons against the baseline of the current tagger.
- I2. Noise filter: keep only projected tags where the alignment and a bigram check agree, as the paper's cleanup does. Decide by `en folds`, not by one run.
- I3. Learning curve as a planning tool: plot tagging accuracy against the number of hand-labelled sentences, as the paper does, before adding a new historical book to tools/gutenberg. Run it first on the EWT dev section to test the method.

## For Mova

The chapters are [09. Early Modern and archaic forms](https://md.mova.elamur.ai/free/en/docs/languages/en/09-early-modern-and-archaic/) and [10. Word classes](https://md.mova.elamur.ai/free/en/docs/languages/en/10-word-classes/). The code is `en/src/ptag.rs` and `tools/treeagree`. The PPCME data is NC: use it only for tests. Measure first how many tokens of a verse-aligned lesson keep the same tag after projection.

Topics: [Part-of-speech tagging](https://md.mova.elamur.ai/free/en/docs/papers/topics/part-of-speech-tagging/)

## Cite

The paper itself, not this card. BibTeX from the library's record of the work.

```
@inproceedings{moon2007part,
  title = {{Part-of-Speech Tagging for Middle English through Alignment and Projection of Parallel Diachronic Texts}},
  author = {Moon, Taesun and Baldridge, Jason},
  booktitle = {Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL)},
  year = {2007},
  url = {https://aclanthology.org/D07-1041/}
}
```

- [← previous Frustratingly Hard Domain Adaptation for Dependency Parsing](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2007-dredze-frustratingly-hard-domain-adaptation-dependency/)
- [next → Adapting a WSJ-Trained Parser to Grammatically Noisy Text](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2008-foster-adapting-wsj-trained-parser-grammatically/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## References

19 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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