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
- I1.
en/src/morph.rs: lemmatization as a class of edit rules (strip a suffix, add a suffix), learned fromdata/forms.tsvanddata/lemmas.tsv, used only for forms that the dictionary does not contain. Measure lemma accuracy on the unseen forms of EWT test against the current rule set. - I2. Report the lemma accuracy on unseen words separately, as the paper does, since the English dictionary hides this error on known words.
- I3. A joint score: add the tag-lemma pair probability of
en/src/ptag.rsand the lemma rule, and keep the best pair. Measure joint accuracy on EWT test, and check that two runs give the same output.
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
@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
- Alexandru Ceauşu. 2006. Maximum Entropy Tiered Tagging. In Proceedings of ESSLLI 2006 Student Session. find
- Grzegorz Chrupała. 2006. Simple data-driven context-sensitive lemmatization. Procesamiento del Lenguaje Natural, (37):121–127. find
- 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
- Tomaž Erjavec and Sašo Džeroski. 2004. Machine learning of morphosyntactic structure: Lemmatizing unknown Slovene words. Applied Artificial Intelligence, 18:17–41. find
- Tomaž Erjavec. 2004. MULTEXT-East version 3: Multilingual morphosyntactic specifications, lexicons and corpora. In Fourth Intl. Conf. on Language Resources and Evaluation. find
- Nizar Habash and Owen Rambow. 2005. Arabic tokenization, part-of-speech tagging and morphological disambiguation in one fell swoop. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics, pages 573–580. find our card
- Jan Hajič and Barbora Hladká. 1998. Tagging inflective languages: prediction of morphological categories for a rich, structured tagset. In Proceedings of the 17th international conference on Computational linguistics, pages 483–490. find
- Jan Hajič. 2000. Morphological tagging: data vs. dictionaries. In Proceedings of the first conference on North American chapter of the Association for Computational Linguistics, pages 94–101. find
- 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
- Chung-Hye Han and Martha Palmer. 2004. A morphological tagger for korean: Statistical tagging combined with corpus-based morphological rule application. Machine Translation, 18(4):275–297. find
- 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. ↗
- Eugene W. Myers. 1986. An O(ND) difference algorithm and its variations. Algorithmica, 1(1):251–266, March. find
- 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
- 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
- Dan Tufiş. 1999. Tiered tagging and combined language models classifiers. In TSD ’99: Proceedings of the Second International Workshop on Text, Speech and Dialogue, pages 28–33. find
- 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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