---
title: "A Language-Independent Feature Schema for Inflectional"
description: "Comparing or transferring inflectional morphology across languages is hard because each language… 37 references, cited by 57 papers here."
authors: ["John Sylak-Glassman", "Christo Kirov", "David Yarowsky", "Roger Que"]
year: 2015
venue: "Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)"
source: "https://aclanthology.org/P15-2111/"
doi: "10.3115/v1/p15-2111"
licence: "CC BY-NC-SA 3.0 (ACL Anthology, before 2016)"
licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/glassman-2015-language-independent-feature-schema-inflectional/"
---
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# A Language-Independent Feature Schema for Inflectional Morphology

**Authors:** [John Sylak-Glassman](https://md.research.elamur.ai/free/en/authors/john-sylak-glassman/), [Christo Kirov](https://md.research.elamur.ai/free/en/authors/christo-kirov/), [David Yarowsky](https://md.research.elamur.ai/free/en/authors/david-yarowsky/) and [Roger Que](https://md.research.elamur.ai/free/en/authors/roger-que/) · **Year:** 2015 · **Venue:** [Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2015](https://md.research.elamur.ai/free/en/venues/acl/2015/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY-NC-SA 3.0 (ACL Anthology, before 2016)](https://creativecommons.org/licenses/by-nc-sa/3.0/)

**Links:** [Publisher's page](https://aclanthology.org/P15-2111/) · [PDF](https://aclanthology.org/P15-2111.pdf) · [DOI 10.3115/v1/p15-2111](https://doi.org/10.3115/v1/p15-2111) · [OpenAlex](https://openalex.org/W2250273070)

**Citations:** 59 citations (OpenAlex, 2026-10-14) · [57](https://md.research.elamur.ai/free/en/papers/glassman-2015-language-independent-feature-schema-inflectional/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Comparing or transferring inflectional morphology across languages is hard because each language and resource uses its own labels. The authors address this with a universal feature schema built from linguistic typology. Its features are "semantic atoms" that describe the meanings carried by overt, affixal inflection, and they can combine to describe any such morpheme. Unlike UD or Interset, which harmonize existing tagsets for a limited set of languages, the schema is meant to define meanings for any language without depending on a particular one.

To populate it, they built a general parser for Wiktionary inflection tables. The parser links each form to its column, row and corner headers and ranks those headers by distance. A mapping step then converts the extracted descriptors into schema features. The output is a large, standardized collection of paradigms.

When this data was fed to Durrett and DeNero's (2013) paradigm learner, accuracy matched what their hand-built, language-specific parsers achieved. The general parser, however, covered far more languages. Larger training sets also brought significant gains for morphologically complex languages, showing the resource is useful for multilingual morphological analysis.

## Key points
- The schema covers 23 dimensions of meaning (e.g., case, aspect, evidentiality, politeness) with 212 features. Labels follow the Leipzig Glossing Rules where possible, and gender is handled as an open class.
- Wiktionary extraction yielded 883,965 instantiated paradigms in 352 languages, 130 of which have more than 100 lemmas.
- With Durrett and DeNero's (2013) learner, 123 language-POS pairs (88 languages) could be analyzed, compared with 5 pairs (3 languages) in the original work.
- Increasing training data from 500 lemmas to the full set (always more than 2000) gave statistically significant gains (p \< 0.05) for 14 of 32 language-POS pairs.
- The largest improvements (p \< 0.001) were for Latin verbs, Finnish verbs, Icelandic nouns and Hungarian nouns. Simple cases, such as Romance noun plurals, needed little data to begin with.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: John Sylak-Glassman, Christo Kirov, David Yarowsky and Roger Que. 2015. A Language-Independent Feature Schema for Inflectional Morphology. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers).

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

```bibtex
@inproceedings{sylakglassman2015language,
  title = {{A Language-Independent Feature Schema for Inflectional Morphology}},
  author = {John Sylak-Glassman, Christo Kirov, David Yarowsky and Roger Que},
  booktitle = {Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  year = {2015},
  doi = {10.3115/v1/p15-2111},
  url = {https://doi.org/10.3115/v1/p15-2111}
}
```

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*Updated 24 October 2026*