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Middle English Open Syllable Lengthening (MEOSL) or Middle English Compensatory Lengthening (MECL)?

Published online by Cambridge University Press:  26 October 2020

DONKA MINKOVA
Affiliation:
Department of English UCLA University of California, Los Angeles 149 Kaplan Hall Los Angeles, CA 90095 USA minkova@HUMNET.UCLA.EDU
MICHAEL LEFKOWITZ
Affiliation:
357 Castro St Suite 200 Mountain View, CA 94041 USA lmlefkowitz@gmail.com
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Abstract

This study addresses a controversial aspect of the change traditionally known as Middle English Open Syllable Lengthening (MEOSL): the variable results of lengthening in disyllabic (C)V.CVC stems, the heavenhaven conundrum. It presents a full philological survey of the recoverable monomorphemic input items and their reflexes in Present-day English (PDE). A re-examination of the empirical data reveals a previously unnoticed correlation between lengthening and the sonority of the medial consonant in forms such as paper, rocket, gannet and baron, as well as interplay between that consonant and the σ2 coda. The alignment of disyllabic stems with a medial alveolar stop and a sonorant weak syllable coda (Latin, better, otter) with (C)V.RVR stems (baron, felon, moral) opens up a new perspective on the reconstruction of tapping in English. The results of lengthening in disyllabic forms, including those previously thought of as ‘exceptions’ to the change, are modeled in Classical OT and Maxent OT, prompting an account which reframes MEOSL as a stem-level compensatory process (MECL) for all inputs. We show that OT grammars with conventional constraints can correctly predict variation in the (C)V.TəR stems and categorical lengthening or non-lengthening in other disyllabic stems. Broadening the phonological factors beyond the open-syllable condition for potential stressed σ1 inputs in (C)V.CV(C) stems allows us to apply the same constraints to stems whose input structure does not involve an open syllable and to propose a uniform account of stressed vowel quantity in all late Middle English mono- and di-syllabic stems.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike licence (http://creativecommons.org/licenses/by-nc-sa/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the same Creative Commons licence is included and the original work is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
Copyright © The Author(s) 2020
Figure 0

Figure 1. OSL and post-tonic syllable preservation as percentage9

Figure 1

Figure 2. Reflexes of (C)V.CəR inputs

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Table 1. Medial consonants in (C)V.CəR inputs to OSL

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Figure 3. Medial consonants and lengthened/unlengthened (C)V.CəR inputs

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Figure 4. Sonorant vs obstruent medial consonants in (C)V.CəR inputs to OSL

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Table 2. Overall patterns of lengthening in input disyllables

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Table 3. Constraints and definitions

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Table 4. OT tableau for (C)VCə, (C)VCVV

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Table 5. Constraint definition for ID(syllabic)

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Table 6. OT tableau for (C)VTəR

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Table 7. Constraint definitions for apocope and *VVCC

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Table 8. OT tableau for (C)VVTTə, (C)VVTə, (C)VTəT

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Table 9. Constraint definition for the Sonority Sequencing Principle

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Table 10. OT tableau for all (C)VCV(C) forms

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Table 11. Constraint ranking and strata learned by OTSoft for tableaux which differ only in whether lengthening applies to (C)VTəR forms

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Figure 5. Hasse diagrams for the ‘paper’ grammar in which (C)VTəR words show lengthening (left) and for the ‘copper’ grammar in which (C)VTəR words do not show lengthening (right). In the latter, the dotted lines indicate rankings which need not all be present. In particular, any of the following would suffice to predict the data: Max-V > *ə, ID[long] > *ə, or ID[syll], Max-μ > *ə

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Table 12. Candidate counts given to OTSoft for fitting the weights of a maxent grammar (candidates given counts of 0 are not shown in this table)

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Table 13. Constraint weights learned by OTSoft

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Table 14. Maxent tableau with weights learned by OTSoft. h = harmony (the weighted sum of a candidate's violations), Pr = predicted probability