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Learning Morphology with Morfette

What it is about

Morfette learns morphological tagging and lemmatization jointly from annotated corpora. Two modules, each a maximum entropy classifier, predict the morphological tags and the lemmas. A third module combines their predictions into a probability distribution over tag-lemma pairs for each sentence. Lemmatization is recast as classification: each class label encodes how a word form maps to its lemma, as a change of prefix and suffix. The system is tested on three morphologically rich languages, Romanian, Spanish and Polish, with no language-specific feature engineering. The lemma rules give a relative error reduction of 62% to 66.5% over a baseline for all words, and the paper reports that the baseline is clearly worse on unseen words.

Ideas

For Mova

The code is en/src/morph.rs and en/src/ptag.rs, with the commands en morph-eval and en tag-eval. The chapter is 11. Nouns for plural lemmas and 13. Verbs for verb forms. The paper's languages are not English and its corpora are LREC data under NC, so use it only for the method and test on EWT test. Measure first with a two-fold check (en folds).

Cite

The paper itself, not this card. BibTeX from the library's record of the work. DOI 10.63317/46dhmje42wcr

@inproceedings{chrupala2008learning,
  title = {{Learning Morphology with Morfette}},
  author = {Chrupala, Grzegorz and Dinu, Georgiana and Genabith, Josef van},
  booktitle = {Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC'08)},
  year = {2008},
  doi = {10.63317/46dhmje42wcr},
  url = {https://doi.org/10.63317/46dhmje42wcr}
}

References

16 references, 1 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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  2. Grzegorz Chrupała. 2006. Simple data-driven context-sensitive lemmatization. Procesamiento del Lenguaje Natural, (37):121–127. find
  3. Walter Daelemans, Jakub Zavrel, Antal van den Bosch, and Ko van der Sloot. 2007. MBT: Memory-Based Tagger, version 3.1, Reference Guide. ILK Technical Report Series 07-08. find
  4. Tomaž Erjavec and Sašo Džeroski. 2004. Machine learning of morphosyntactic structure: Lemmatizing unknown Slovene words. Applied Artificial Intelligence, 18:17–41. find
  5. Tomaž Erjavec. 2004. MULTEXT-East version 3: Multilingual morphosyntactic specifications, lexicons and corpora. In Fourth Intl. Conf. on Language Resources and Evaluation. find
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  9. Dilek Z. Hakkani-Tür, Kemal Oflazer, and Gokhan Tür. 2002. Statistical Morphological Disambiguation for Agglutinative Languages. Computers and the Humanities, 36(4):381–410. find
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  11. M. Antonia Martı́, Mariona Taulé, Lluı́s Márquez, and Manuel Bertran. 2007. CESS-ECE: A multilingual and multilevel annotated corpus. Available from: http://www.lsi.upc.edu/˜mbertran/cess-ece. ↗
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  13. Adwait Ratnaparkhi. 1996. A maximum entropy model for part-of-speech tagging. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. find
  14. Dan Tufiş and Liviu Dragomirescu. 2004. Tiered Tagging Revisited. In Proc. of the Fourth Intl. Conf. on Language Resources and Evaluation, LREC, pages 39–42. find
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  16. Deniz Yuret and Ferhan Türe. 2006. Learning morphological disambiguation rules for Turkish. In Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, pages 328–334. find

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