--- title: "Very-large Scale Parsing and Normalization of Wiktionary Morphological Paradigms — Kirov 2016" description: "The paper describes a language-independent extractor that reads the paradigm tables of Wiktionary straight from its… 7 references, cited by 48 papers here." authors: ["Christo Kirov", "John Sylak-Glassman", "Roger Que", "David Yarowsky"] year: 2016 venue: "LREC 2016 (Portorož)" source: "https://doi.org/10.63317/5nt2dyri2un4" doi: "10.63317/5nt2dyri2un4" licence: "CC BY-NC-SA 3.0" licence_url: "https://creativecommons.org/licenses/by-nc-sa/3.0/" summary_licence: "Elamur Cosmic License" canonical: "https://research.elamur.ai/free/en/papers/kirov-2016-wiktionary-paradigms/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kirov-2016-wiktionary-paradigms/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2016](https://md.research.elamur.ai/free/en/papers/2016/page/3/) / Very-large Scale Parsing and Normalization of Wiktionary Morphological Paradigms # Very-large Scale Parsing and Normalization of Wiktionary Morphological Paradigms **Authors:** [Christo Kirov](https://md.research.elamur.ai/free/en/authors/christo-kirov/), [John Sylak-Glassman](https://md.research.elamur.ai/free/en/authors/john-sylak-glassman/), Roger Que and [David Yarowsky](https://md.research.elamur.ai/free/en/authors/david-yarowsky/) · **Year:** 2016 · **Venue:** Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) **Link:** [aclanthology.org/L16-1498](https://aclanthology.org/L16-1498/) · **Licence:** [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/) (LREC proceedings) — summary only [**56** citations](https://openalex.org/works?filter=cites:W2574182335) OpenAlex, 2026-10-14 · [**48** citations in our library](https://md.research.elamur.ai/free/en/papers/kirov-2016-wiktionary-paradigms/cited-by/) of the 140,884 papers whose bibliographies we read · [LREC 2016 (Portorož)](https://openalex.org/S7407088301) · [licence](https://creativecommons.org/licenses/by-nc-sa/3.0/) - [Publisher's page](https://doi.org/10.63317/5nt2dyri2un4) - [PDF](https://aclanthology.org/L16-1498.pdf) - [DOI 10.63317/5nt2dyri2un4](https://doi.org/10.63317/5nt2dyri2un4) - [OpenAlex](https://openalex.org/W2574182335) **Authors (4)** 1. [Christo Kirov](https://openalex.org/A5063051593) [Semantic Scholar](https://www.semanticscholar.org/author/2022649) · h 17 · 1,343 citations · 50 works 2. John Sylak-Glassman 3. [Roger Que](https://openalex.org/A5062574211) [Semantic Scholar](https://www.semanticscholar.org/author/2043874) · h 3 · 139 citations · 3 works 4. [David Yarowsky](https://openalex.org/A5015876016) [Semantic Scholar](https://www.semanticscholar.org/author/1693517) · h 53 · 12,502 citations · 199 works Citations and author metrics: OpenAlex, 2026-10-14; authors with the same name are not always told apart. ## What it is about The paper describes a language-independent extractor that reads the paradigm tables of Wiktionary straight from its HTML. It separates inflected forms from grammatical descriptors by token frequency across lexemes, and maps the descriptors to one annotation scheme with 212 features in 23 dimensions. The output has 952,530 unique noun, verb and adjective lemmas across 350 languages, and about 1,000,000 paradigms overall. The authors report 123 usable language-POS pairs in 88 languages, against 5 pairs in 3 languages for an earlier language-specific extractor. The English edition often gives forms only as human-readable instructions, so its tables need separate handling. ## Ideas - I3. Layout consistency check: group English lemma tables by layout and count how many layouts give more than one feature reading. Use this as an offline check on the lexicon build, not as a training source. ## For Mova - I1. `en/src/lexgen.rs`: the cross-lexeme test (a token that recurs across many lemma tables is a descriptor, not a form) can flag suspect entries in `data/forms.tsv`. Measure how many entries it flags and hand-check a sample of 100 flagged entries before any entry is removed. - I2. `en/src/morph.rs`: a descriptor table that maps each written tag to one feature set, as the paper's unified scheme does. Measure on EWT test the share of gold morphological features the table can express, and report it per feature dimension. The code is `en/src/lexgen.rs` and `en/src/morph.rs`, with `en/src/udreg.rs` for the feature names. The check is `en morph-eval`. Wiktionary text is CC BY-SA, and the extracted resource is for tests and comparisons only; never carry the extracted tables into the build. Measure first: the lemma and feature accuracy of `en morph-eval` on EWT test, with and without the flagged entries removed, on the two-fold check. ## Institutions [🎓**Johns Hopkins University** Baltimore · United States 151 works · 84,111 citations](https://md.research.elamur.ai/free/en/universities/johns-hopkins-university/) Topics[Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) ## Cite Cite as Christo Kirov, John Sylak-Glassman, Roger Que and David Yarowsky. 2016. Very-large Scale Parsing and Normalization of Wiktionary Morphological Paradigms. LREC 2016 (Portorož). The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.63317/5nt2dyri2un4](https://doi.org/10.63317/5nt2dyri2un4) ```bibtex @inproceedings{kirov2016very, title = {{Very-large Scale Parsing and Normalization of Wiktionary Morphological Paradigms}}, author = {Christo Kirov, John Sylak-Glassman, Roger Que and David Yarowsky}, booktitle = {LREC 2016 (Portorož)}, year = {2016}, doi = {10.63317/5nt2dyri2un4}, url = {https://doi.org/10.63317/5nt2dyri2un4} } ``` - [← previous Analyzing lexical emergence in Modern American English online](https://md.research.elamur.ai/free/en/papers/grieve-2016-lexical-emergence-american-english/) - [next → SemEval-2016 Task 10: Detecting Minimal Semantic Units and their Meanings (DiMSUM)](https://md.research.elamur.ai/free/en/papers/schneider-2016-semeval-2016-task-10-detecting/) - [↑ back to papers of 2016](https://md.research.elamur.ai/free/en/papers/2016/page/3/) ## References 7 references, 2 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. Choi, J., de Marneffe, M.-C., Dozat, T., Ginter, F., Goldberg, Y., Hajič, J., Manning, C., McDonald, R., Nivre, J., Petrov, S., Pyysalo, S., Silveira, N., Tsarfaty, R., and Zeman, D. (2015). Universal Dependencies. Accessible at: [http://universaldependencies.github.io/docs/](http://universaldependencies.github.io/docs/), January. [↗](http://universaldependencies.github.io/docs/) 2. Durrett, G. and DeNero, J. (2013). Supervised learning of complete morphological paradigms. In Proceedings of the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1185–1195. Association for Computational Linguistics, A… [our card](https://md.research.elamur.ai/free/en/papers/durrett-2013-morphological-paradigms/) 3. Liebeck, M. and Conrad, S. (2015). IWNLP: Inverse Wiktionary for natural language processing. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (ACL-IJCNLP), pages 414–418, Beijing, Jul… [find](https://scholar.google.com/scholar?q=Liebeck%2C+M%2E+and+Conrad%2C+S%2E+%282015%29%2E+IWNLP%3A+Inverse+Wiktionary+for+natural+language+processing%2E+In+Proceedings+of+the+53rd+Annual+Meeting+of+the+Association+for+Computational+Linguistics+and+the+7th+Int) 4. Sylak-Glassman, J., Kirov, C., Post, M., Que, R., and Yarowsky, D. (2015a). A universal feature schema for rich morphological annotation and fine-grained cross-lingual part-of-speech tagging. 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(2008). Reusable tagset conversion using tagset drivers. In Proceedings of LREC 2008, pages 213–218. [our card](https://md.research.elamur.ai/free/en/papers/zeman-2008-reusable-tagset-conversion-tagset-drivers/) 7. Lang English Italian Finnish French Serbo-Croatian Japanese Spanish German Portuguese Dutch Esperanto Latin Russian Swedish Hungarian Greek Polish Georgian Irish Catalan Latvian Armenian Icelandic Romanian Norwegian Bokmål Korean Scottish Gaelic Norwegian Nynorsk Ido Old French Old Armenian Danish… [find](https://scholar.google.com/scholar?q=Lang+English+Italian+Finnish+French+Serbo%2DCroatian+Japanese+Spanish+German+Portuguese+Dutch+Esperanto+Latin+Russian+Swedish+Hungarian+Greek+Polish+Georgian+Irish+Catalan+Latvian+Armenian+Icelandic+Rom)