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New ways of analyzing complementizer drop in Montréal French: Exploration of cognitive factors

Published online by Cambridge University Press:  04 March 2022

Yiming Liang*
Affiliation:
Université de Paris, CNRS, Laboratoire de linguistique formelle
Pascal Amsili
Affiliation:
Sorbonne Nouvelle, Laboratoire Lattice (CNRS/PSL-ENS/SN)
Heather Burnett
Affiliation:
Université de Paris, CNRS, Laboratoire de linguistique formelle
*
*Corresponding author. E-mail: yiming.liang@etu.u-paris.fr
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Abstract

In this paper, we return to the well-studied yet still puzzling phenomenon of complementizer omission in a large spoken corpus of Quebec French, with the help of modern computational methods for annotation and mixed effects logistic regression models. Supporting previous work, our study reveals that complementizer que omission is conditioned by social factors and grammatical factors; however, we also find that que omission is conditioned by cognitive factors such as information density. Our paper thus illustrates an important way in which older variationist corpora can continue to be valuable resources for studying fine-grained patterns of variation, particularly in their cognitive aspects.

Information

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press
Figure 0

Figure 1. Illustration of the information density in bits per word in time for two French CCs with (dotted lines) and without (dashed lines) the complementizer que. Figures (a) and (b) respectively show the information density of a nonpredictable CC embedded by the matrix verb savoir ‘know’ and a predictable CC embedded by penser ‘think.’

Figure 1

Table 1. Verbs chosen for the study, ordered by CC-bias. Frequency = Frequency of the verb lemma in the corpus, CC = number of occurrences of CCs, CC-bias3 = verb's subcategorization bias for a CC, O = number of que omissions

Figure 2

Figure 2. Mean predicted probabilities versus observed proportions of omitted que. The data are grouped by speakers and the diagonal line represents a perfect match between predicted and actual proportions.

Figure 3

Table 2. Result summary: coefficient estimates β, standard errors SE, z value, p value and significance level indicated by stars * for all the variables in the model. A positive coefficient means that the first level correlates with a higher rate of que-omission than the second (number of CCs = 5818, number of que-omission cases = 1441)

Figure 4

Table 3. Que-omission rate, number of omission cases and number of CCs for each predictor level

Figure 5

Figure 3. Effect of the matrix verb's CC bias on the omission of the complementizer que, along with 95% confidence interval (shaded area). The dots represent matrix verbs, and the line indicates the linear model.12

Figure 6

Figure 4. Right phonological context versus que omission (with 95% confidence interval).

Figure 7

Figure 5. CC subject versus que omission (with 95% confidence interval).

Figure 8

Table 4. Results of comparison between the full model (m1) and the model without right phonological context (m2)

Figure 9

Table 5. Results of comparison between the full model (m1) and the model without CC subject (m3)

Figure 10

Table 6. Cross-tabulation of right phonological context and CC subject

Figure 11

Figure 6. que-drop rate across CC subject and right phonological context groupings.16

Figure 12

Figure 7. Profession versus que omission.

Figure 13

Figure 8. Education level versus que omission.