[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/aclbulk-2009-jongejan-automatic-training-lemmatization-rules-that/) [home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [English](https://md.mova.elamur.ai/free/en/docs/papers/en/) / Automatic training of lemmatization rules that handle morphological changes in pre-, in- and suffixes alike # Automatic training of lemmatization rules that handle morphological changes in pre-, in- and suffixes alike **Authors:** Bart Jongejan · **Year:** 2009 · **Venue:** Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP **Link:** [aclanthology.org/P09-1017](https://aclanthology.org/P09-1017/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only [**70** citations](https://openalex.org/works?filter=cites:W2141457256) openalex-snapshot · 2026-10-14[**20** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2009-jongejan-automatic-training-lemmatization-rules-that/) of the 140673 papers whose bibliographies we read[Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP](https://aclanthology.org/volumes/P09-1/)[licence](https://creativecommons.org/licenses/by-nc-sa/3.0/) - [Publisher's page](https://aclanthology.org/P09-1017/) - [PDF](https://aclanthology.org/P09-1017.pdf) - [DOI 10.3115/1687878.1687900](https://doi.org/10.3115/1687878.1687900) - [OpenAlex](https://openalex.org/W2141457256) **Authors (2)** 1. [Bart Jongejan](https://openalex.org/A5079747112) [Semantic Scholar](https://www.semanticscholar.org/author/2645334) · h 6 · 189 citations · 57 works 2. [Hercules Dalianis](https://openalex.org/A5010279607) [ORCID](https://orcid.org/0000-0003-0165-9926) · [Semantic Scholar](https://www.semanticscholar.org/author/1772465) · h 30 · 3480 citations · 211 works Citations and author metrics from openalex-snapshot (2026-10-14); authors with the same name are not always told apart. ## What it is about Most lemmatizers only cut suffixes, which fails when a word changes in its prefix or in its middle. The author trains lemmatization rules automatically from full-form and lemma pairs. The rules can change prefixes, infixes and suffixes, and the lemmatizer applies them to a new full form. The method is trained on ten languages: Danish, Dutch, English, German, Greek, Icelandic, Norwegian, Polish, Slovene and Swedish. Compared with a suffix-only lemmatizer, it improves by 24% for Polish, 2.3% for Dutch, 1.5% for English, 1.2% for German and 1.0% for Swedish. Icelandic gets worse by 1.9%. The paper also reports how the number of rules grows with the number of training pairs. ## Ideas - I1. `en/src/morph.rs`: learn lemma rules that can change a prefix or infix from `data/forms.tsv` and `data/lemmas.tsv`, and use them only for forms that the dictionary does not cover. Measure lemma accuracy on the uncovered forms of EWT test, against the suffix-only rules. - I2. Count the rules against the training pairs, as the paper does, and print the rule count per run. A rule count that keeps growing with the pairs is a sign of over-fitting, and it can be checked before a claim. - I3. Test the paper's English gain of 1.5% on Mova's own form table: remove a random tenth of the English forms, learn the rules, and measure how many removed lemmas the rules recover. The result is a Mova number, not the paper's. ## For Mova The code is `en/src/morph.rs` and `en/src/lexgen.rs`, with `en morph-eval`. The chapter is [06. Spelling rules: inflection, derivation, compounds and national standards](https://md.mova.elamur.ai/free/en/docs/languages/en/06-spelling-rules/), and for prefixes [16. Derivation I: prefixes](https://md.mova.elamur.ai/free/en/docs/languages/en/16-derivation-prefixes/). English gains here are small, so the value is the method for prefix changes, which English has in few words. Measure first on EWT test, twice, with a two-fold check (`en folds`). ## Cite The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1687878.1687900](https://doi.org/10.3115/1687878.1687900) ``` @inproceedings{jongejan2009automatic, title = {{Automatic training of lemmatization rules that handle morphological changes in pre-, in- and suffixes alike}}, author = {Jongejan, Bart and Dalianis, Hercules}, booktitle = {Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP}, year = {2009}, doi = {10.3115/1687878.1687900}, url = {https://doi.org/10.3115/1687878.1687900} } ``` - [← previous Evaluating a Statistical CCG Parser on Wikipedia](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2009-honnibal-evaluating-statistical-ccg-parser-wikipedia/) - [next → Detecting Compositionality in Multi-Word Expressions](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2009-korkontzelos-detecting-compositionality-multi-word-expressions/) - [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/) ## References 24 references, 0 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet. 1. 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