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A Multi-purpose Bayesian Model for Word-Based Morphology

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Systems and Frameworks for Computational Morphology (SFCM 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 537))

Abstract

This paper introduces a probabilistic model of morphology based on a word-based morphological theory. Morphology is understood here as a system of rules that describe systematic correspondences between full word forms, without decomposing words into any smaller units. The model is formulated in the Bayesian learning framework and can be trained in both supervised and unsupervised setting. Evaluation is performed on tasks of generating unseen words, lemmatization and inflected form production.

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Notes

  1. 1.

    The notion of root used here has nothing to do with the definition typically used in morphology. It is meant as a root of a derivational tree, like the one shown in Fig. 1, which principally can be any word.

  2. 2.

    The distribution of set length has negligible influence on the behavior of the model and is included only for formal completeness. Poisson distribution is chosen because of mathematical simplicity.

  3. 3.

    In our formalism, rules are functions mapping words to sets of words. The set is empty if the constraints on the left-hand side of the rule are not met. Otherwise, typically a single word is produced, but cases with more than one result are also possible.

  4. 4.

    For simplicity, it is assumed here that the newly inserted word does not take over any child nodes from other words.

  5. 5.

    Although ber is not a valid German word, it may happen to occur in the data, for example as an abbreviation or a foreign word.

  6. 6.

    http://corpora.uni-leipzig.de.

  7. 7.

    http://sjp.pl/slownik/odmiany/.

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Authors and Affiliations

  1. Institute of Computer Science, University of Leipzig, Augustusplatz 10, 04109, Leipzig, Germany

    Maciej Janicki

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  1. Maciej Janicki
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Correspondence to Maciej Janicki .

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Editors and Affiliations

  1. Institut für Deutsche Sprache, Mannheim, Germany

    Cerstin Mahlow

  2. Leibniz Institute of European History, Mainz, Germany

    Michael Piotrowski

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Janicki, M. (2015). A Multi-purpose Bayesian Model for Word-Based Morphology. In: Mahlow, C., Piotrowski, M. (eds) Systems and Frameworks for Computational Morphology. SFCM 2015. Communications in Computer and Information Science, vol 537. Springer, Cham. https://doi.org/10.1007/978-3-319-23980-4_7

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  • DOI: https://doi.org/10.1007/978-3-319-23980-4_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23978-1

  • Online ISBN: 978-3-319-23980-4

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