--- title: "Knowledge-Free Induction of Inflectional Morphologies — Schone 2001" description: "The paper induces the morphology of an inflectional language from raw text, with no human input. 17 references, cited by 52 papers here." authors: ["Patrick Schone", "Daniel Jurafsky"] year: 2001 venue: "Second Meeting of the North American Chapter of the Association for Computational Linguistics" source: "https://aclanthology.org/N01-1024/" doi: "10.3115/1073336.1073360" 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/schone-2001-knowledge-free-induction-inflectional-morphologies/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/schone-2001-knowledge-free-induction-inflectional-morphologies/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2001](https://md.research.elamur.ai/free/en/papers/2001/) / Knowledge-Free Induction of Inflectional Morphologies # Knowledge-Free Induction of Inflectional Morphologies **Authors:** Patrick Schone · **Year:** 2001 · **Venue:** Second Meeting of the North American Chapter of the Association for Computational Linguistics (NAACL 2001) **Link:** [aclanthology.org/N01-1024](https://aclanthology.org/N01-1024/) · **Licence:** [CC BY-NC-SA 3.0](https://creativecommons.org/licenses/by-nc-sa/3.0/) — summary only [**118** citations](https://openalex.org/works?filter=cites:W1983311927) OpenAlex, 2026-10-14 · [**52** citations in our library](https://md.research.elamur.ai/free/en/papers/schone-2001-knowledge-free-induction-inflectional-morphologies/cited-by/) of the 140,884 papers whose bibliographies we read · [Second Meeting of the North American Chapter of the Association for Computational Linguistics](https://aclanthology.org/volumes/N01-1/) · [licence](https://creativecommons.org/licenses/by-nc-sa/3.0/) - [Publisher's page](https://aclanthology.org/N01-1024/) - [PDF](https://aclanthology.org/N01-1024.pdf) - [DOI 10.3115/1073336.1073360](https://doi.org/10.3115/1073336.1073360) - [OpenAlex](https://openalex.org/W1983311927) **Authors (2)** 1. [Patrick Schone](https://openalex.org/A5067979421) [Semantic Scholar](https://www.semanticscholar.org/author/1686074) · h 12 · 874 citations · 39 works 2. [Daniel Jurafsky](https://openalex.org/A5036266399) [Semantic Scholar](https://www.semanticscholar.org/author/1746807) · h 50 · 19,011 citations · 115 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 induces the morphology of an inflectional language from raw text, with no human input. The algorithm combines three cues: orthography, semantics, and syntactic distributions. Its output is a set of conflation sets, groups of the inflected and derived forms of one word. The method is applied to German, Dutch and English. Using the hand-labelled CELEX lexicon as the gold standard, the current version reaches an F-score of 88.1% for conflation sets in English, which the author reports as better than the earlier knowledge-free algorithms. The paper also lists the precision of the suffix pairs it finds, mostly between about 70% and 96%. ## Ideas - I3. Add a negative control: shuffle the words of the corpus so that the distributional cue is gone, and show that the F-score falls. This tests whether the semantic cue is doing the work. ## For Mova - I1. `en/src/lexgen.rs`: group the forms of `data/forms.tsv` that are not yet linked to a lemma into conflation sets from a raw English corpus, using the orthographic and distributional cues of the paper. Measure the F-score of the sets against the dictionary on a held-out part of the forms. - I2. Use the suffix-pair table as a candidate list for the rules in `en/src/morph.rs`: each pair from raw text with a high count becomes a candidate rule, and the rule is accepted only if it is right on the held-out forms. The code is `en/src/lexgen.rs` and `en/src/morph.rs`, with the commands `en lexicon` and `en morph-eval`. The chapters are [languages/en/16-derivation-prefixes.md](https://mova.elamur.ai/free/en/docs/languages/en/16-derivation-prefixes/) and [languages/en/17-derivation-suffixes.md](https://mova.elamur.ai/free/en/docs/languages/en/17-derivation-suffixes/). CELEX is a licensed lexicon, so it is test only. Measure first on held-out English forms, twice, with a two-fold check (`en folds`). ## Institutions [🎓**University of Colorado Boulder** Boulder · United States 41 works · 48,832 citations](https://md.research.elamur.ai/free/en/universities/university-of-colorado-boulder/) Topics[Morphology](https://md.research.elamur.ai/free/en/topics/morphology/) ## Cite Cite as Patrick Schone and Daniel Jurafsky. 2001. Knowledge-Free Induction of Inflectional Morphologies. Second Meeting of the North American Chapter of the Association for Computational Linguistics. The paper itself, not this card. BibTeX from the library's record of the work. [DOI 10.3115/1073336.1073360](https://doi.org/10.3115/1073336.1073360) ```bibtex @inproceedings{schone2001knowledge, title = {{Knowledge-Free Induction of Inflectional Morphologies}}, author = {Patrick Schone and Daniel Jurafsky}, booktitle = {Second Meeting of the North American Chapter of the Association for Computational Linguistics}, year = {2001}, doi = {10.3115/1073336.1073360}, url = {https://doi.org/10.3115/1073336.1073360} } ``` - [← previous Classifying the Semantic Relations in Noun Compounds via a Domain-Specific Lexical Hierarchy](https://md.research.elamur.ai/free/en/papers/rosario-2001-classifying-semantic-relations-noun-compounds/) - [next → A Corpus-Based Evaluation of Centering and Pronoun Resolution](https://md.research.elamur.ai/free/en/papers/tetreault-2001-corpus-based-evaluation-centering-pronoun/) - [↑ back to papers of 2001](https://md.research.elamur.ai/free/en/papers/2001/) ## References 17 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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