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Measuring productivity diachronically: nominal suffixes in English letters, 1400–16001

Published online by Cambridge University Press:  06 November 2014

CHRIS C. PALMER*
Affiliation:
Department of English, Kennesaw State University, 1000 Chastain Road, MD 2701, Kennesaw, Georgia 30144, USAcpalme20@kennesaw.edu
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Abstract

Much scholarship on morphological productivity has focused on measures such as hapax legomena, single occurrences of derivatives in large corpora, to compare and contrast the varying productivities of English affixes. But the small size of historical corpora has often limited the usefulness of such measures in diachronic analysis. Examining letters from the fifteenth and sixteenth centuries in the Corpus of Early English Correspondence, this article advances a multifaceted approach to assessing historical changes in nominal suffixation in English. It adapts methodologies from work on morphological productivity in contemporary language – in particular, measures of base and derivative ratios from Hay & Baayen (2002) – to provide quantitative and qualitative descriptions of changes in the productivity of native -ness and borrowed -ity, -cion, -age and -ment in Early Modern English. Ultimately, the study argues that diachronic productivity is best evaluated with a multifactor analysis, including measures of suffixal decomposability, aggregation of new derivatives and evidence of hybridization. It also suggests that increased use of neologisms with borrowed suffixes in Early Modern English might be explained by the increasing transparency of these suffixes in derivatives during the fifteenth and sixteenth centuries.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2014 
Figure 0

Figure 1. Frequency of derivatives of each suffix type in the Early Middle English sections of the Helsinki Corpus. Each bar represents the number of tokens per 10,000 words

Figure 1

Figure 2. Frequency of derivatives of each suffix type in each subperiod of the CEEC. Each bar represents the number of tokens per 10,000 words

Figure 2

Table 1. Raw counts of new types in each period of the CEEC

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Table 2. Type-parsing percentages in different subperiods of the CEEC

Figure 4

Table 3. Token-parsing percentages in different subperiods of the CEEC

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Table 4. Mean parsing ratios in different subperiods of the CEEC

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Table 5. Summary of productivity findings based on fifteenth- and sixteenth- century data from the CEEC