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When Nobody Wins

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Competition in Inflection and Word-Formation

Part of the book series: Studies in Morphology ((SUMO,volume 5))

Abstract

We argue that lexical gaps arise when language learners fail to find productive rules in a morphological domain. Using the Tolerance Principle as a formal model of productivity, we show that lexical gaps can be predicted on purely numerical grounds using lexical statistics, with case studies on Spanish, Polish, and Russian. The learnability approach taken here leads to simpler theories of morphology.

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Notes

  1. 1.

    This chapter adapts and expands upon analyses first presented in chapter 5 of Yang (2016). We thank Margaret Borowczyk for assistance with Polish, and Jennifer Preys and Vitaly Nikolaev for assistance with Russian. We are also grateful for the comments and suggestions provided by Wolfgang Dressler, Gregory Stump, and the editors of the present volume.

  2. 2.

    For a quantitative analysis of the *stridden gap and the failings of frequency and other measures of indirect negative evidence as a solution to the gap problem, see Yang (2016), section 5.1.1.

  3. 3.

    We avoid the competing term paradigm (or paradigmatic) gap, as we do not wish to make a commitment to the theoretical notion of the inflectional paradigm.

  4. 4.

    Orgun and Sprouse (1999) propose a different, albeit roughly equivalent, mechanism for encoding defectivity in Optimality Theory; see Fanselow and Féry (2002) and Rice and Blaho (2009) for discussion of these mechanisms.

  5. 5.

    See also chapter 5 of Yang (2016) for criticisms of a more subtle, frequency-based, form of lexical conservatism that uses indirect negative evidence.

  6. 6.

    We do not claim that the language user will activate the full lexical entries for the irregulars before accessing the regulars. It is conceivable that the computational search process deals with a hashed list, as described in (1): the entries are “keys” indexed by the semantic and phonological properties of words, which point to full lexical entries in long-term memory, rather than lexical entries themselves.

  7. 7.

    Baayen’s formulation unfortunately, and tautologically, quantifies productivity using an inherently continuous measure, a ratio.

  8. 8.

    Albright et al. (2001) argue that there is some weak segmental conditioning on the application of diphthongization. Even if this is correct—and the evidence is not overwhelming—it does not obviate the need for lexical diacritics.

  9. 9.

    These e-i alternations are also present in deverbal derivatives (Harris 1969, 110; Lerner 2011).

  10. 10.

    This correctly predicts that this pattern will be restricted to the third conjugation, since only this conjugation has an -i- theme vowel needed to condition the lowering rule.

  11. 11.

    Note that the g-j alternation in the conjugation of sumergir is purely orthographic; both are [x] throughout.

  12. 12.

    Many Spanish verb stems have multiple prefixal variants; for instance, desvestir ‘to undress’, investir ‘to invest’, revestir ‘to decorate’, and tra(s)vestir(se) ‘to cross-dress’ are all plausibly derived from vestir ‘to dress’. Without exception, all verbs derived from the same stem undergo the same set of stem changes, and therefore we collect counts over verb stems rather than verbs. Verbs were manually grouped by stem, with an etymological dictionary (Roberts 2014) used to adjudicate unclear cases.

  13. 13.

    We analyze the three conjugations separately because it has long been theorized that the productivity of the various stem changes may be a function of conjugation. Note, however, that the Tolerance predictions are the same if the first two conjugations are grouped together.

  14. 14.

    Whereas in Table 3 we collapse prefixal variants of a stem into a single entry, Albright does not mention any such practice, so inputs to the MGL system in this experiment are verb stems plus any prefix(es), rather than stems.

  15. 15.

    However, it may still be the case that subjective frequencies used by Albright are better predictors of defectivity than the corpus-based frequency norms used here.

  16. 16.

    This calculation is complicated slightly by a small number of masculine nouns which take -a in the gen.sg. and -u in the dative singular. Since this word list does not provide a way to determine the case of individual tokens, the count of -u nouns (516) may be a slight overestimate, but it is unlikely this would change the results, as the number of nouns potentially affected is rather small and both classes are quite far from the productivity threshold.

  17. 17.

    The only clear exception is the labial stem zatmit’ ‘to eclipse’. Moskvin (2015, cited in Pertsova 2016) argues this gap is due to phonotactic illformedness.

  18. 18.

    Pertsova (2016) distinguishes between t-stems and st-stems, presumably because the s of the latter is never present in the 1sg. non-past; e.g., vyrastit’-vyrašču ‘raise, cultivate’, but *vyra[stʃj]u. However, this can be handled with an additional—surface-true—phonological rule simplifying the resulting sibilant cluster, as [stʃj] is not a valid onset in Russian (Vitaly Nikolaev, p.c.).

  19. 19.

    As in Spanish, many Russian verbs are derived from prefixation to stems, but with rare exceptions, all derivatives of a stem undergo the same mutation in the 1sg. non-past. Therefore, Pertsova (2016) collects counts over stems rather than full verbs.

  20. 20.

    Pertsova (ibid.) counts 38 non-defective s → š stems and 36 non-defective z → ž stems. Assuming these are related by a single rule of retroflexion, as seems likely, they can tolerate no more than 20 non-alternating exceptions.

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Authors and Affiliations

  1. Department of Linguistics, Graduate Center of the City University of New York, New York, NY, USA

    Kyle Gorman

  2. Google, Inc., New York, NY, USA

    Kyle Gorman

  3. Department of Linguistics, University of Pennsylvania, Philadelphia, PA, USA

    Charles Yang

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Editors and Affiliations

  1. Institute of Romance Languages, WU Vienna, Vienna, Austria

    Franz Rainer

  2. Department of Comparative Linguistics, University of Zurich, Zurich, Switzerland

    Francesco Gardani

  3. Austrian Centre for Digital Humanities, Austrian Academy of Sciences, Vienna, Austria

    Wolfgang U. Dressler

  4. Department of Linguistics, University of Vienna, Vienna, Austria

    Hans Christian Luschützky

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Gorman, K., Yang, C. (2019). When Nobody Wins. In: Rainer, F., Gardani, F., Dressler, W., Luschützky, H. (eds) Competition in Inflection and Word-Formation . Studies in Morphology, vol 5. Springer, Cham. https://doi.org/10.1007/978-3-030-02550-2_7

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