Abstract
In the field of Learner Corpus Research, Gries and Deshors (Corpora 9(1):109–136, 2014) developed a two-step regression procedure (MuPDAR) to determine how and why choices made by non-native speakers differ from those made by native speakers more comprehensively than traditional learner corpus research allows for. In this chapter, we will extend and test their proposal to determine whether it can also be applied to pragmatic and grammatical phenomena (subject realization/omission in Japanese), and whether it can help study categorical differences between learner and native-speaker choices; we do so by also showing that the more advanced method of mixed-effects modeling can be very fruitfully integrated into the proposed MuPDAR method. The results of our study show that Japanese native speakers’ choices of subject realization are affected by discourse-functional factors such as givenness and contrast of referents and that, while learners are able to handle extreme values of givenness and marked cases of contrast, they still struggle (more) with intermediate degrees of givenness and unmarked/non-contrastive referents. We conclude by discussing the role of MuPDAR in Learner Corpus Research in general and its advantages over traditional corpus analysis in that field and error analysis in particular.
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Notes
- 1.
We are disregarding here the large body of multifactorial work done by Crossley, Jarvis, and collaborators (cf. in particular Jarvis & Crossley 2012) because much of that work focuses on detecting the L1 of a writer rather than, as here, understanding any one particular lexical or grammatical choice in detail.
- 2.
An additional problem may involve the fact that the authors used a linear regression on data that might violate the assumptions of such regressions. However, we were unable to infer from the paper what the dependent variable was – possibly a frequency of ser/estar + adjective per file? – so the above has to remain speculation for now.
- 3.
We thank Nobutaka Takara and Mikuni Okamoto for their help in transcribing the corpus data.
- 4.
By virtue of the complexity of the statistical methods involved, this section can only be rather technical in nature, plus space constraints do not permit exhaustive definitions and discussion of all the statistical technical terms. We therefore refer the reader to Baayen (2008: Ch. 7), Crawley (2013: Ch. 9, 19), Faraway (2006: Ch. 8–10), and Zuur et al. (2009: Ch. 5).
- 5.
Strictly speaking, if one does a MuPDAR analysis in which R1 is really only used for prediction, then one does not really have to apply Occam's razor rigorously to eliminate non-significant/collinear predictors that much because, within MuPDAR, the point of R1 is not to actually interpret R1's coefficients.
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Gries, S.T., Adelman, A.S. (2014). Subject Realization in Japanese Conversation by Native and Non-native Speakers: Exemplifying a New Paradigm for Learner Corpus Research. In: Romero-Trillo, J. (eds) Yearbook of Corpus Linguistics and Pragmatics 2014. Yearbook of Corpus Linguistics and Pragmatics, vol 2. Springer, Cham. https://doi.org/10.1007/978-3-319-06007-1_3
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