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The nominative-to-accusative shift in Japanese: diachronic and synchronic considerations

  • Satoshi Nambu ORCID logo EMAIL logo , David Y. Oshima ORCID logo and Shin-ichiro Sano


Some Japanese predicates allow their object to be marked either with the accusative particle o, the canonical marker of an object, or with the nominative particle ga, the canonical marker of a subject. This work reports and discusses the results of corpus-based surveys on the patterns and recent trends in nominative-marking on objects. It will be demonstrated: (i) that there has been an increase in the proportion of accusative-marking over nominative-marking in recent times, (ii) that accusative-marking is more likely to be chosen in certain types of subordinate clauses than in matrix clauses, and (iii) that at least with some types of predicates, accusative-marking on the object is more likely when its referent is animate rather than inanimate. It will be argued that the effects of clause type and animacy have to do with the functional motivation to mitigate processing load and the risk of misinterpretation incurred by nominative-marking on objects.

Corresponding author: Satoshi Nambu, Monash University, School of Languages, Literatures, Cultures and Linguistics, Menzies Building, 20 Chancellors Walk, Clayton 3800, Australia, E-mail:


Part of this research was presented at the 26th Japanese/Korean Linguistics conference held at UCLA in 2018. We would like to thank Yoshiki Ogawa and the two anonymous reviewers of Journal of Japanese Linguistics for their valuable comments. We would also like to acknowledge that this research greatly benefited from the NINJAL collaborative research project “Development of and Linguistic Research with a Parsed Corpus of Japanese”.

  1. Research funding: This work was supported by JSPS KAKENHI ( Grant Number JP15J00283.

Appendix: The lack of dependence between the effects of the factors considered

We reported some statistical findings on (i) how the birth year correlates with the proportions of ga- and o-marking on the object in different types of NA/CN-predicates, (ii) how the clause type correlates with the proportions of ga- and o-marking on the object in different types of NA/CN-predicates, and (iii) how the animacy of the referent of the object correlates with the proportions of ga- and o-marking on the object in different types of NA/CN-predicates. The findings concerning (i) and (ii) are based on two sets of data, one from BCCWJ and the other from CSJ; those concerning (iii) are based on the BCCWJ data only (the CSJ data did not suit our purpose due to their relatively small size).

In purpose to confirm that the effects of the relevant factors are independent from each other, we constructed two logistic regression models, one based on the BCCWJ data and the other based on the CSJ data. The BCCWJ-based model was constructed with four factors: (i) birth year, (ii) predicate type (derived potential, phrasal potential, desiderative, and simple CN-predicate), (iii) clause type (main clause, complement clause, relative clause, temporal/conditional clause, reason clause, and the remainder), and (iv) animacy of the referent of the object (animate and inanimate). The CSJ-based model was built with the same set of factors but animacy of the referent of the object.

It was confirmed that, in the BCCWJ-based model, the effects of the four factors are not dependent on one another and that, in the CSJ-based model, the effects of the three factors are not dependent on one another (Tables A and B).

Table A:

Results from the BCCWJ-based logistic regression model.

Estimate Standard error z p
Intercept 10.77 3.69 2.92 <0.01
Birth year −0.006 0.002 −3.07 <0.01
Predicate type
 Desideratives −3.15 0.14 −23.11 <0.001
 Simple CN-predicates 2.65 0.13 19.62 <0.001
 Phrasal potentials −1.45 0.11 −12.73 <0.001
Clause type
Main 0.77 0.20 3.86 <0.001
 Remainder −0.08 0.20 −0.38 0.71
Reason 0.72 0.32 2.26 0.02
Rel. −0.65 0.24 −2.69 0.07
T/C 0.17 0.28 0.63 0.53
 Inanimate 1.30 0.18 7.11 <0.001
Table B:

Results from the CSJ-based logistic regression model.

Estimate Standard error z p
Intercept 15.77 8.53 1.85 0.07
Birth year −0.01 0.004 −2.21 <0.05
Predicate type
 Derived potentials 3.92 0.21 18.44 <0.001
 Simple CN-predicates 6.42 0.25 26.02 <0.001
 Phrasal potentials 3.08 0.21 14.44 <0.001
Clause type
Main 0.98 0.21 4.60 <0.001
 Remainder 0.19 0.18 1.04 0.30
Reason 0.57 0.35 1.65 0.10
Rel. −0.09 0.28 −0.33 0.74
T/C 0.48 0.39 1.25 0.21


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Received: 2021-12-15
Accepted: 2022-03-05
Published Online: 2022-09-21
Published in Print: 2022-11-25

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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