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# MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology

**Authors:** [Khuyagbaatar Batsuren](https://md.mova.elamur.ai/free/en/docs/papers/authors/khuyagbaatar-batsuren/), Gábor Bella, Fausto Giunchiglia · **Year:** 2021 · **Venue:** Proceedings of the Seventeenth SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology **Link:** [aclanthology.org/2021.sigmorphon-1.5](https://aclanthology.org/2021.sigmorphon-1.5/) · **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)

[**25** citations](https://openalex.org/works?filter=cites:W3185008966) openalex · 2026-10-18[**33** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/lex-2021-batsuren-morphynet/) of the 140673 papers whose bibliographies we read[SIGMORPHON 2021 (ACL workshop)](https://openalex.org/S7407088808)[licence](https://creativecommons.org/licenses/by/4.0/)

- [Publisher's page](https://doi.org/10.18653/v1/2021.sigmorphon-1.5)
- [PDF](https://aclanthology.org/2021.sigmorphon-1.5.pdf)
- [DOI 10.18653/v1/2021.sigmorphon-1.5](https://doi.org/10.18653/v1/2021.sigmorphon-1.5)
- [OpenAlex](https://openalex.org/W3185008966)

**Authors (3)**

1. [Khuyagbaatar Batsuren](https://openalex.org/A5086221076) [ORCID](https://orcid.org/0000-0002-6819-5444) · [Semantic Scholar](https://www.semanticscholar.org/author/2049136) · h 13 · 777 citations · 53 works
2. [Gabor Bella](https://openalex.org/A5075478426) [ORCID](https://orcid.org/0000-0002-3868-1740) · [Semantic Scholar](https://www.semanticscholar.org/author/21693058) · h 15 · 993 citations · 85 works
3. [Fausto Giunchiglia](https://openalex.org/A5001227032) [ORCID](https://orcid.org/0000-0002-5903-6150) · [Semantic Scholar](https://www.semanticscholar.org/author/1720285) · h 52 · 16265 citations · 599 works

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

## What it is about

MorphyNet is a multilingual database of morphology built from Wiktionary. Its first version covers 15 languages with 519k derivational and 10.1M inflectional entries, each with lemma, part of speech, morphological tags and affixes. The extraction uses hand-written rules for Wiktionary inflection tables and headword lines, plus an enrichment step that copies derivation patterns across languages. Enrichment adds 25.6% to the number of derivations; for English it adds only 3%, against 57% for Spanish. Manual checks on 1,000 sampled entries per language give 98.9% precision overall and 99.5% for English.

## Ideas

- I1. `en/src/morph.rs`: take the English derivation pairs as a reference list of affixed forms and their bases. Measure how many of them the current lemma module already maps to the same base on EWT test forms. The pairs are Wiktionary-derived, so they are test data only.
- I2. `en/src/lexgen.rs`: add a counted suffix rule for English derivations (for example -ation from -ate or -e) that is accepted only when the paper-style evidence count passes a threshold. Measure precision and recall of the new derivations against the English reference pairs, on a held-out split.
- I3. Sample audit: draw 100 derived forms from `en` output, check them by hand, and report the precision with its interval, as the paper does with its 1,000-entry sample. Use it as a quality gate before any new derivation rule is added.

## For Mova

The chapters are [17. Derivation II: suffixes](https://md.mova.elamur.ai/free/en/docs/languages/en/17-derivation-suffixes/) and `16-derivation-prefixes.md`. The code is `en/src/morph.rs` and `en/src/lexgen.rs`, and the check is `en morph-eval`. The paper's English gain is small, so the English value comes from the checks, not from the data. Its data is derived from Wiktionary, so use it for tests and as a reference only, never in what is built. Measure first: the share of English reference derivations that `en morph-eval` already reproduces.

## Cite

The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.18653/v1/2021.sigmorphon-1.5](https://doi.org/10.18653/v1/2021.sigmorphon-1.5)

```
@inproceedings{batsuren2021morphynet,
  title = {{MorphyNet: a Large Multilingual Database of Derivational and Inflectional Morphology}},
  author = {Batsuren, Khuyagbaatar and Bella, Gabor and Giunchiglia, Fausto},
  booktitle = {SIGMORPHON 2021 (ACL workshop)},
  year = {2021},
  doi = {10.18653/v1/2021.sigmorphon-1.5},
  url = {https://doi.org/10.18653/v1/2021.sigmorphon-1.5}
}
```

- [← previous Wiktionary Normalization of Translations and Morphological Information](https://md.mova.elamur.ai/free/en/docs/papers/lex-2020-wu-wiktionary-normalization/)
- [next → Superbizarre Is Not Superb: Derivational Morphology Improves BERT's Interpretation of Complex Words](https://md.mova.elamur.ai/free/en/docs/papers/lex-2021-hofmann-superbizarre-derivational/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

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3. A major piece of ongoing work concerns the representation of MorphyNet derivational data as a lexico-semantic graph, as it is done in wordnets (Miller, 1998; Giunchiglia et al., 2017) where derivationally related word senses are interconnected by associative relationships. This effort, justifying th… [find](https://scholar.google.com/scholar?q=A+major+piece+of+ongoing+work+concerns+the+representation+of+MorphyNet+derivational+data+as+a+lexico%2Dsemantic+graph%2C+as+it+is+done+in+wordnets+%28Miller%2C+1998%3B+Giunchiglia+et+al%2E%2C+2017%29+where+derivation)
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