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# EtymoLink: A Structured English Etymology Dataset

**Authors:** Yuan Gao, Weiwei Sun · **Year:** 2024 · **Venue:** Proceedings of the Workshop on Computational Approaches to Historical Language Change (LChange 2024) **Link:** [aclanthology.org/2024.lchange-1.12](https://aclanthology.org/2024.lchange-1.12/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**1** citations](https://openalex.org/works?filter=cites:W4402670486) openalex · 2026-10-18LChange 2024 (ACL workshop, Bangkok)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://doi.org/10.18653/v1/2024.lchange-1.12)
- [PDF](https://aclanthology.org/2024.lchange-1.12.pdf)
- [DOI 10.18653/v1/2024.lchange-1.12](https://doi.org/10.18653/v1/2024.lchange-1.12)
- [OpenAlex](https://openalex.org/W4402670486)

**Authors (2)**

1. [Yuan Gao](https://openalex.org/A5107840513) [ORCID](https://orcid.org/0009-0002-6397-2854) · [Semantic Scholar](https://www.semanticscholar.org/author/2315317158) · h 12 · 612 citations · 107 works
2. [Weiwei Sun](https://openalex.org/A5100952935) [ORCID](https://orcid.org/0009-0005-6372-0325) · [Semantic Scholar](https://www.semanticscholar.org/author/2315592255) · h 2 · 45 citations · 11 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 authors turn the prose etymologies of the Online Etymology Dictionary (Etymonline) into structured relations between word roots. They hand-annotate a subset of entries as ground truth and fine-tune FLAN-T5-base to extract the relations for the rest. The resulting dataset has over 103,000 relationships over 63,603 English terms, with 15,931 connected components. On the root-relationship evaluation the system reaches 94.4% accuracy. Language labels are harder: detection is 0.990, but label accuracy is 0.909. Output in the right format covers 98.2% of entries once the language-label constraint is dropped. The authors also report that more training data mainly helps the label-based metrics.

## Ideas

- I1. `en/src/morph.rs` and `en/src/dict.rs`: load the paper's relations (root, affix, source language) as a table keyed by lemma, and use it as a check on the lemma and affix analysis. Measure how many lemmas in the EWT test set get a relation, and how often the morph analysis agrees with the relation.
- I2. Deterministic extractor: write a Rust pattern matcher over the Etymonline-style phrases ("from X, Y, Z") in a raw dump kept in `$MOVA_DATA`, and compare its root accuracy with the paper's 94.4% on a hand-checked sample of 200 entries. This turns the neural step into a readable rule set.
- I3. Connected-component test: count the components and the most connected affixes (the paper's top five are all affixes) and check that `en/src/morph.rs` treats those affixes as affixes, not as roots.

## For Mova

The touch points are `en/src/morph.rs`, `en/src/dict.rs` and a rule matcher in `tools/`. The chapters are [16. Derivation I: prefixes](https://md.mova.elamur.ai/free/en/docs/languages/en/16-derivation-prefixes/) and [17. Derivation II: suffixes](https://md.mova.elamur.ai/free/en/docs/languages/en/17-derivation-suffixes/), with [09. Early Modern and archaic forms](https://md.mova.elamur.ai/free/en/docs/languages/en/09-early-modern-and-archaic/) for the old source languages. The dataset is CC BY 4.0, but it is derived from Etymonline, so check that source's terms before loading it into the build. Measure first the agreement of the morph analysis with the relations on the EWT lemmas.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2024.lchange-1.12](https://doi.org/10.18653/v1/2024.lchange-1.12)

```
@inproceedings{gao2024etymolink,
  title = {{EtymoLink: A Structured English Etymology Dataset}},
  author = {Gao, Yuan and Sun, Weiwei},
  booktitle = {LChange 2024 (ACL workshop, Bangkok)},
  year = {2024},
  doi = {10.18653/v1/2024.lchange-1.12},
  url = {https://doi.org/10.18653/v1/2024.lchange-1.12}
}
```

- [← previous WordNet under Scrutiny: Dictionary Examples in the Era of Large Language Models](https://md.mova.elamur.ai/free/en/docs/papers/lex-2024-almeman-wordnet-examples-llm/)
- [next → Do Large Language Models Understand Word Senses?](https://md.mova.elamur.ai/free/en/docs/papers/lex-2025-meconi-llms-word-senses/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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