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# A semiautomatic lemmatisation procedure for treebanks. Old English strong and weak verbs

**Authors:** Marta Tío Sáenz and Darío Metola Rodríguez · **Year:** 2017 · **Venue:** Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories (TLT16) **Link:** [aclanthology.org/W17-7613](https://aclanthology.org/W17-7613/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) (ACL Anthology)

[Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories](https://aclanthology.org/volumes/W17-76/)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://aclanthology.org/W17-7613/)
- [PDF](https://aclanthology.org/W17-7613.pdf)

**Authors (2)**

1. Marta Tío Sáenz
2. Darío Metola Rodríguez

Citations and author metrics from OpenAlex (); authors with the same name are not always told apart.

## What it is about

Old English treebanks have syntax and morphology but no lemmas, so searches cannot group the forms of one verb. The paper describes a semi-automatic lemmatiser, Norna, that works on a database of about 190,000 inflected forms from the Dictionary of Old English corpus. For the seven strong verb classes, a reference list of verbs is expanded into all their forms. Four query strings run in sequence, from exact stems to stems with any prefix, and then four filters cut the hits. Filter 1 keeps verbal endings and reduces 17,138 hits to 1,939. Weak verbs of classes 1 and 2 are handled by 24 and 29 ending patterns against lists of more than 2,000 verbs. Checked against the Dictionary of Old English for letters A–H, the strong-verb procedure is about 80% accurate before manual revision. The weak-verb work yields more than 4,680 forms for class 1 and more than 6,600 for class 2, and the dictionary comparison adds about 12,000 extra forms and recurring spelling patterns (e for y, o for a, ge for gi).

## Ideas

- I1. `en/src/morph.rs`: a rule-based lemmatiser for the Early Modern strong verbs, with the forms generated from a verb list and the endings -est, -eth, -th (hath, doth) handled by the closed table in `en/src/emode.rs`. Measure lemma accuracy on a hand-checked set against the paper's 80% bar.
- I2. Filter cascade with counts: each filter must report how many hits it removes, and a negative control must show that a filter does not remove a set of known true forms.
- I3. Keep a spelling-pattern table (vowel swaps and unaccented endings) as data in [2. Spelling, print conventions and tokens](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/02-spelling-and-tokens/), updated only from errors seen in a checked dictionary, so each pattern has a source.

## For Mova

Chapters: [4. Verb endings: -est, -st, -eth, -th](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/04-verb-endings/), [6. Strong verbs, old pasts and participles, old plurals](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/06-strong-verbs/), [2. Spelling, print conventions and tokens](https://md.mova.elamur.ai/free/en/docs/languages/en-x-emode/02-spelling-and-tokens/). Code: `en/src/morph.rs`, `en/src/emode.rs`. Measure: lemma accuracy on a checked list of Early Modern verb forms, the reduction at each filter, and the same run twice. The method is deterministic, which suits Mova's rules. The paper's Old English data are not Mova data; use the method, not the lists.

Topics: [Treebanks](https://md.mova.elamur.ai/free/en/docs/papers/topics/treebanks/)

## Cite

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

```
@inproceedings{saenz2017semiautomatic,
  title = {{A semiautomatic lemmatisation procedure for treebanks. Old English strong and weak verbs}},
  author = {Sáenz, Marta Tío and Rodríguez, Darío Metola},
  booktitle = {Proceedings of the 16th International Workshop on Treebanks and Linguistic Theories},
  year = {2017},
  url = {https://aclanthology.org/W17-7613/}
}
```

- [← previous A Dictionary and Morphological Analyser for English](https://md.mova.elamur.ai/free/en/docs/papers/russell-1986-dictionary-morphological-analyser-english/)
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15 references, 0 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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