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Article published In:
Studies in Language
Vol. 46:4 (2022) ► pp.753–792

Derivation predicting inflection

A quantitative study of the relation between derivational history and inflectional behavior in Latin

| Université de Paris, CNRS, Laboratoire de linguistique formelle
ORCID logo | Università Cattolica del Sacro Cuore
In this paper, we investigate the value of derivational information in predicting the inflectional behavior of lexemes. We focus on Latin, for which large-scale data on both inflection and derivation are easily available. We train boosting tree classifiers to predict the inflection class of verbs and nouns with and without different pieces of derivational information. For verbs, we also model inflectional behavior in a word-based fashion, training the same type of classifier to predict wordforms given knowledge of other wordforms of the same lexemes. We find that derivational information is indeed helpful, and document an asymmetry between the beginning and the end of words, in that the final element in a word is highly predictive, while prefixes prove to be uninformative. The results obtained with the word-based methodology also allow for a finer-grained description of the behavior of different pairs of cells.
Keywords: morphology, derivation, inflection, Latin
Article outline
  • 1.Introduction
  • 2.Data collection and annotation
  • 3.Predicting inflection classes
    • 3.1Methodology
    • 3.2Predicting the conjugation of verbs
    • 3.3Predicting the declension of nouns
  • 4.A word-based alternative: Predicting verb forms
    • 4.1Rationale
    • 4.2The information-theoretic approach to the PCFP
    • 4.3The PCFP as a classification problem
    • 4.4Results
  • 5.Conclusions
  • Acknowledgements
  • Notes
  • Appendix
  • References
Published online: 21 January 2022
DOI logo
https://doi.org/10.1075/sl.21002.bon
References (38)
References
Ackerman, Farrell, James P. Blevins & Robert Malouf. 2009. Parts and wholes: Patterns of relatedness in complex morphological systems and why they matter. In James P. Blevins & Juliette Blevins (eds.), Analogy in grammar: Form and acquisition, 54–82. Oxford: Oxford University Press. DOI logoGoogle Scholar
Ackerman, Farrell & Robert Malouf. 2013. Morphological organization: The low conditional entropy conjecture. Language 891. 429–464. DOI logoGoogle Scholar
Albright, Adam C. & Bruce P. Hayes. 2003. Rules vs. analogy in English past tenses: A computational/experimental study. Cognition 901. 119–161. DOI logoGoogle Scholar
Aronoff, Mark. 1994. Morphology by itself: Stems and inflectional classes. Cambridge: MIT press.Google Scholar
Beniamine, Sacha. 2018. Classifications flexionnelles. Étude quantitative des structures de paradigmes. Paris: Université Sorbonne Paris Cité-Université Paris Diderot PhD dissertation.
Beniamine, Sacha, Olivier Bonami & Benoît Sagot. 2017. Inferring inflection classes with description length. Journal of Language Modelling 5(3). 465–525.Google Scholar
Blevins, James P. 2016. Word and paradigm morphology. Oxford: Oxford University Press. DOI logoGoogle Scholar
Bonami, Olivier & S. Beniamine. 2016. Joint predictiveness in inflectional paradigms. Word Structure 9(2). 156–182. DOI logoGoogle Scholar
Bonami, Olivier & Gilles Boyé. 2006. Deriving inflectional irregularity. In Stefan Müller (ed.), Proceedings of the 13th International Conference on Head Driven Phrase Structure Grammar, Varna, 361–380. Stanford, CA: CSLI Publications. [URL]
. 2014. De formes en thèmes. In Florence Villoing, Sarah Leroy & Sophie David (eds.), Foisonnements morphologiques. Études en hommage à Françoise Kerleroux, 17–45. Paris: Presses Universitaires de Paris-Ouest.Google Scholar
Boyé, Gilles & Gauvain Schalchli. 2019. Realistic data and paradigms: The paradigm cell finding problem. Morphology 291. 199–248. DOI logoGoogle Scholar
Brown, Dunstan, Marina Chumakina & Greville G. Corbett. 2013. Canonical morphology and syntax. Oxford: Oxford University Press.Google Scholar
Brown, Dunstan & Andrew Hippisley. 2012. Network morphology: A defaults based theory of word structure. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Budassi, Marco & Eleonora Litta. 2017. In trouble with the rules. Theoretical issues raised by the insertion of -sc-verbs into Word Formation Latin. In Proceedings of the Workshop on Resources and Tools for Derivational Morphology (DeriMo), 15–26.Google Scholar
Bybee, Joan. 1995. Regular morphology and the lexicon. Language and Cognitive Processes 10(5). 425–455. DOI logoGoogle Scholar
Corbett, Greville G. 2005. The canonical approach in typology. In Zygmunt Frajzyngier, Adam Hodges & David S. Rood (eds.), Linguistic diversity and language theories, 25–49. Amsterdam: John Benjamins. DOI logoGoogle Scholar
2009. Canonical inflectional classes. In Fabio Montermini, Gilles Boyé & Jesse Tseng (eds.), Selected proceedings of the 6th Décembrettes: Morphology in Bordeaux, 1–11. Somerville: Cascadilla Proceedings Project.Google Scholar
Delatte, Louis, Étienne Evrard, Suzanne Govaerts & Joseph Denooz. 1981. Dictionnaire fréquentiel et index inverse de la langue latine. Liège: LASLA.Google Scholar
Dressler, Wolfgang U. 2002. Latin inflection classes. In A. Machtelt Bolkestein, Caroline H. M. Kroon, Harm Pinkster, H. Wim Remmelink & Rodie Risselada (eds.), Theory and description in Latin linguistics, 91–110. Leiden: Brill.Google Scholar
Friedman, Jerome H. 2001. Greedy function approximation: A gradient boosting machine. Annals of statistics, 1189–1232. DOI logoGoogle Scholar
Guzmán Naranjo, Matías. 2019. Analogical classification in formal grammar. Berlin: Language Science Press.Google Scholar
. 2020. Analogy, complexity and predictability in the Russian nominal inflection system. Morphology 30(3). 219–262. DOI logoGoogle Scholar
Guzmán Naranjo, Matías & Olivier Bonami. 2021. Overabundance and inflectional classification: quantitative evidence from Czech. Glossa 61. DOI logoGoogle Scholar
Litta, Eleonora & Marco Passarotti. 2020. (When) inflection needs derivation: A word formation lexicon for Latin. In Nigel Holmes, Marijke Ottink, Josine Schrickx & Maria Selig (eds.), Lemmata Linguistica Latina. Vol. 11: Words and Sounds, 224–239. Berlin: De Gruyter Mouton.Google Scholar
Malouf, Robert. 2017. Abstractive morphological learning with a recurrent neural network. Morphology 27(4). 431–458. DOI logoGoogle Scholar
Mason, Llew, Jonathan Baxter, Peter L. Bartlett & Marcus R. Frean. 2000. Boosting algorithms as gradient descent. In Advances in Neural Information Processing Systems, 512–518.Google Scholar
McGillivray, Barbara & Adam Kilgarriff. 2013. Tools for historical corpus research, and a corpus of Latin. New Methods in Historical Corpus Linguistics (3). 247–257.Google Scholar
Passarotti, Marco, Marco Budassi, Eleonora Litta & Paolo Ruffolo. 2017. The Lemlat 3.0 package for morphological analysis of Latin. In Gerlof Bouma & Yvonne Adesam (eds.), Proceedings of the NoDaLiDa 2017 Workshop on Processing Historical Language, 24–31. Gothenburg: Linköping University Electronic Press.Google Scholar
Pedregosa, Fabian, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Alexandre Passos, David Cournapeau, Matthieu Brucher, Matthieu Perrot & Édouard Duchesnay. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research 121. 2825–2830.Google Scholar
Pellegrini, Matteo. 2020. Predictability in Latin inflection. An entropy-based approach. Bergamo/Pavia: Università di Bergamo-Università di Pavia PhD dissertation.
. 2021. Patterns of interpredictability and principal parts in Latin verb paradigms: An entropy-based approach. Journal of Latin Linguistics 19(2). 195–221. DOI logoGoogle Scholar
Pellegrini, Matteo & Marco Passarotti. 2018. LatInfLexi: An inflected lexicon of Latin verbs. In Proceedings of the Fifth Italian Conference on Computational Linguistics (CLiC-it 2018). DOI logoGoogle Scholar
Pierrehumbert, Janet. 2001. Stochastic phonology. Glot International 5(6). 195–207.Google Scholar
Plaster, Keith, Maria Polinsky & Boris Harizanov. 2013. Noun classes grow on trees. In Balthasar Bickel, Lenore A. Grenoble, David A. Peterson & Alan Timberlake (eds.), Language typology and historical contingency: In honor of Johanna Nichols, 153–170. Amsterdam: John Benjamins. DOI logoGoogle Scholar
Sims, Andrea. 2015. Inflectional defectiveness. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Stump, Gregory T. 2001. Inflectional morphology: A theory of paradigm structure. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
2015. Inflection classes. In Matthew Baerman (ed.), The Oxford handbook of inflection, 113–139. Oxford: Oxford University Press.Google Scholar
Stump, Gregory T. & Raphael A. Finkel. 2013. Morphological typology: From word to paradigm. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
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Naranjo, Matías Guzmán & Olivier Bonami
2023. A distributional assessment of rivalry in word formation. Word Structure 16:1 ► pp. 87 ff. DOI logo
Pellegrini, Matteo
2023. The Impact of Derivational Relatedness on Inflectional Predictions. In Paradigm Structure and Predictability in Latin Inflection [Studies in Morphology, 6], ► pp. 145 ff. DOI logo
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2023. Conclusions. In Paradigm Structure and Predictability in Latin Inflection [Studies in Morphology, 6], ► pp. 179 ff. DOI logo

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