[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/aclbulk-2001-schone-knowledge-free-induction-inflectional-morphologies/)

[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/) / 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-snapshot · 2026-10-14[**52** citations in our library](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/aclbulk-2001-schone-knowledge-free-induction-inflectional-morphologies/) of the 140673 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) h 12 · 874 citations · 39 works
2. [Daniel Jurafsky](https://openalex.org/A5036266399) h 50 · 19011 citations · 115 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

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

- 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.
- 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

The code is `en/src/lexgen.rs` and `en/src/morph.rs`, with the commands `en lexicon` and `en morph-eval`. The chapters are [16. Derivation I: prefixes](https://md.mova.elamur.ai/free/en/docs/languages/en/16-derivation-prefixes/) and [17. Derivation II: suffixes](https://md.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`).

## Cite

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)

```
@inproceedings{schone2001knowledge,
  title = {{Knowledge-Free Induction of Inflectional Morphologies}},
  author = {Schone, Patrick and Jurafsky, Daniel},
  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.mova.elamur.ai/free/en/docs/papers/aclbulk-2001-rosario-classifying-semantic-relations-noun-compounds/)
- [next → Unsupervised discovery of morphologically related words based on orthographic and semantic similarity](https://md.mova.elamur.ai/free/en/docs/papers/aclbulk-2002-baroni-unsupervised-discovery-morphologically-related-words/)
- [↑ back to English](https://md.mova.elamur.ai/free/en/docs/papers/en/)

## 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. Baayen, R.H., R. Piepenbrock, and H. van Rijn. (1993) The CELEX lexical database (CD-ROM), LDC, Univ. of Pennsylvania, Philadelphia, PA. [find](https://scholar.google.com/scholar?q=Baayen%2C+R%2EH%2E%2C+R%2E+Piepenbrock%2C+and+H%2E+van+Rijn%2E+%281993%29+The+CELEX+lexical+database+%28CD%2DROM%29%2C+LDC%2C+Univ%2E+of+Pennsylvania%2C+Philadelphia%2C+PA%2E)
2. Brent, M., S. K. Murthy, A. Lundberg. (1995). Discovering morphemic suffixes: A case study in MDL induction. Proc. Of 5 th Int’l Workshop on Artificial Intelligence and Statistics DéJean, H. (1998) Morphemes as necessary concepts for structures: Discovery from untagged corpora. Workshop on paradigms… [find](https://scholar.google.com/scholar?q=Brent%2C+M%2E%2C+S%2E+K%2E+Murthy%2C+A%2E+Lundberg%2E+%281995%29%2E+Discovering+morphemic+suffixes%3A+A+case+study+in+MDL+induction%2E+Proc%2E+Of+5+th+Int%E2%80%99l+Workshop+on+Artificial+Intelligence+and+Statistics+D%C3%A9Jean%2C+H%2E+%281998%29+Mo)
3. Dumais, G.W. Furnas, T.K. Landauer, and R. Harshman. (1990) Indexing by Latent Semantic Analysis. Journal of the American Society of Information Science, Vol. 41, pp.391-407. [find](https://scholar.google.com/scholar?q=Dumais%2C+G%2EW%2E+Furnas%2C+T%2EK%2E+Landauer%2C+and+R%2E+Harshman%2E+%281990%29+Indexing+by+Latent+Semantic+Analysis%2E+Journal+of+the+American+Society+of+Information+Science%2C+Vol%2E+41%2C+pp%2E391%2D407%2E)
4. Gaussier, É. (1999) Unsupervised learning of derivational morphology from inflectional lexicons. ACL '99 Workshop: Unsupervised Learning in Natural Language Processing, Univ. of Maryland. [find](https://scholar.google.com/scholar?q=Gaussier%2C+%C3%89%2E+%281999%29+Unsupervised+learning+of+derivational+morphology+from+inflectional+lexicons%2E+ACL+%2799+Workshop%3A+Unsupervised+Learning+in+Natural+Language+Processing%2C+Univ%2E+of+Maryland%2E)
5. Goldsmith, J. (1997/2000) Unsupervised learning of the morphology of a natural language. Univ. of Chicago. [http://humanities.uchicago.edu/faculty/goldsmith](http://humanities.uchicago.edu/faculty/goldsmith). [↗](http://humanities.uchicago.edu/faculty/goldsmith)
6. Grabar, N. and P. Zweigenbaum. (1999) Acquisition automatique de connaissances morphologiques sur le vocabulaire médical, TALN, Cargése, France. [find](https://scholar.google.com/scholar?q=Grabar%2C+N%2E+and+P%2E+Zweigenbaum%2E+%281999%29+Acquisition+automatique+de+connaissances+morphologiques+sur+le+vocabulaire+m%C3%A9dical%2C+TALN%2C+Carg%C3%A9se%2C+France%2E)
7. Harris, Z. (1951) Structural Linguistics. University of Chicago Press. [find](https://scholar.google.com/scholar?q=Harris%2C+Z%2E+%281951%29+Structural+Linguistics%2E+University+of+Chicago+Press%2E)
8. Jacquemin, C. (1997) Guessing morphology from terms and corpora. SIGIR'97, pp. 156-167, Philadelphia, PA. [find](https://scholar.google.com/scholar?q=Jacquemin%2C+C%2E+%281997%29+Guessing+morphology+from+terms+and+corpora%2E+SIGIR%2797%2C+pp%2E+156%2D167%2C+Philadelphia%2C+PA%2E)
9. Kazakov, D. (1997) Unsupervised learning of naïve morphology with genetic algorithms. In W. Daelemans, et al., eds., ECML/Mlnet Workshop on Empirical Learning of Natural Language Processing Tasks, Prague, pp. 105-111. [find](https://scholar.google.com/scholar?q=Kazakov%2C+D%2E+%281997%29+Unsupervised+learning+of+na%C3%AFve+morphology+with+genetic+algorithms%2E+In+W%2E+Daelemans%2C+et+al%2E%2C+eds%2E%2C+ECML%2FMlnet+Workshop+on+Empirical+Learning+of+Natural+Language+Processing+Tasks%2C+Pra)
10. Landauer, T.K., P.W. Foltz, and D. Laham. (1998) Introduction to Latent Semantic Analysis. Discourse Processes. Vol. 25, pp. 259-284. [find](https://scholar.google.com/scholar?q=Landauer%2C+T%2EK%2E%2C+P%2EW%2E+Foltz%2C+and+D%2E+Laham%2E+%281998%29+Introduction+to+Latent+Semantic+Analysis%2E+Discourse+Processes%2E+Vol%2E+25%2C+pp%2E+259%2D284%2E)
11. Manning, C.D. and H. Schütze. (1999) Foundations of Statistical Natural Language Processing, MIT Press, Cambridge, MA. [find](https://scholar.google.com/scholar?q=Manning%2C+C%2ED%2E+and+H%2E+Sch%C3%BCtze%2E+%281999%29+Foundations+of+Statistical+Natural+Language+Processing%2C+MIT+Press%2C+Cambridge%2C+MA%2E)
12. Nakisa, R.C., U.Hahn. (1996) Where defaults don't help: the case of the German plural system. Proc. of the 18th Conference of the Cognitive Science Society. [find](https://scholar.google.com/scholar?q=Nakisa%2C+R%2EC%2E%2C+U%2EHahn%2E+%281996%29+Where+defaults+don%27t+help%3A+the+case+of+the+German+plural+system%2E+Proc%2E+of+the+18th+Conference+of+the+Cognitive+Science+Society%2E)
13. Schone, P. and D. Jurafsky. (2000) Knowledge-free induction of morphology using latent semantic analysis. Proc. of the Computational Natural Language Learning Conference, Lisbon, pp. 67-72. [find](https://scholar.google.com/scholar?q=Schone%2C+P%2E+and+D%2E+Jurafsky%2E+%282000%29+Knowledge%2Dfree+induction+of+morphology+using+latent+semantic+analysis%2E+Proc%2E+of+the+Computational+Natural+Language+Learning+Conference%2C+Lisbon%2C+pp%2E+67%2D72%2E)
14. Schütze, H. (1993) Distributed syntactic representations with an application to part-of-speech tagging. Proceedings of the IEEE International Conference on Neural Networks, pp. 1504-1509. [find](https://scholar.google.com/scholar?q=Sch%C3%BCtze%2C+H%2E+%281993%29+Distributed+syntactic+representations+with+an+application+to+part%2Dof%2Dspeech+tagging%2E+Proceedings+of+the+IEEE+International+Conference+on+Neural+Networks%2C+pp%2E+1504%2D1509%2E)
15. Sproat, R. (1992) Morphology and Computation. MIT Press, Cambridge, MA. [find](https://scholar.google.com/scholar?q=Sproat%2C+R%2E+%281992%29+Morphology+and+Computation%2E+MIT+Press%2C+Cambridge%2C+MA%2E)
16. Xu, J., B.W. Croft. (1998) Corpus-based stemming using co-occurrence of word variants. ACM Transactions on Information Systems, 16 (1), pp. 61-81. [find](https://scholar.google.com/scholar?q=Xu%2C+J%2E%2C+B%2EW%2E+Croft%2E+%281998%29+Corpus%2Dbased+stemming+using+co%2Doccurrence+of+word+variants%2E+ACM+Transactions+on+Information+Systems%2C+16+%281%29%2C+pp%2E+61%2D81%2E)
17. Yarowsky, D. and R. Wicentowski. (2000) Minimally supervised morphological analysis by multimodal alignment. Proc. of the ACL 2000, Hong Kong. [find](https://scholar.google.com/scholar?q=Yarowsky%2C+D%2E+and+R%2E+Wicentowski%2E+%282000%29+Minimally+supervised+morphological+analysis+by+multimodal+alignment%2E+Proc%2E+of+the+ACL+2000%2C+Hong+Kong%2E)