Experimental evidence suggests that null complement anaphora in Russian is not reducible to clausal ellipsis
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Mikhail Knyazev
Abstract
Null complement anaphora, NCA (e.g., I suggested the price was too high, and she agreed ∅.), is a long known but poorly understood phenomenon subject to idiosyncratic lexical restrictions. In languages like Russian, however, it is (or appears) productive, with verbs not allowing NCA hard to find, raising the question whether omission of the clausal argument (CP) in NCA contexts can be derived by a supposedly regular process of CP/clausal argument ellipsis, which has recently gained attention in the literature, and which independently exists in Russian. This paper tests the prediction of this view whereby selection for CP should be sufficient (and necessary) for NCA-licensing, using two acceptability rating studies based on a corpus-based list of potential NCA-licensors. The results suggest that whether a verb licenses omission of the CP in NCA contexts is not predictable from its ability to license CP, and that the process is lexically sensitive. This provides evidence against analyzing NCA contexts as a result of CP ellipsis and more generally against reducing NCA to the latter in Russian and similar languages. The paper highlights issues of cross-linguistic variation in the study of NCA. It also raises questions about the parallelism condition on clausal ellipsis.
Acknowledgments
I thank the reviewers and the audience of the 14th conference in the series “Typology of Morphosyntactic Parameters” (TMP 2024), where part of this research was presented. I also thank Ivan Pivnev and Alexandra Maslennikova for assisting me in collecting and analyzing corpus data on which this research is partly based (as part of their student practice at HSE University, Saint Petersburg).
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Research ethics: Not applicable.
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Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.
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Author contributions: The author has accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The author states no conflict of interest.
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Research funding: This research is supported by Russian Science Foundation, RSF project #24-28-01873 realized at HSE University.
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Data availability: The raw data are publicly accessible through an OSF repository: https://osf.io/m9nhf/?view_only=42ad70e6b0294b86a58d8c4e9a5489d0.
Abbreviations
- acc
-
accusative case
- adv
-
adverb
- dat
-
dative case
- gen
-
genitive case
- inf
-
infinitive
- ins
-
instrumental case
- m
-
masculine
- pl
-
plural
- prep
-
prepositional case
- ptcp
-
participle
- ptl
-
particle
- q
-
question marker
- sbjv
-
subjunctive
- sg
-
singular
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