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Record W2943921606 · doi:10.1162/ling_a_00342

Null Objects in Korean: Experimental Evidence for the Argument Ellipsis Analysis

2019· article· en· W2943921606 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueLinguistic Inquiry · 2019
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of TorontoSimon Fraser University
Fundersnot available
KeywordsEllipsis (linguistics)LinguisticsArgument (complex analysis)Quantifier (linguistics)AdverbPronounVerbObject (grammar)Raising (metalworking)MathematicsPhilosophy

Abstract

fetched live from OpenAlex

Null object (NO) constructions in Korean and Japanese have received different accounts: as (a) argument ellipsis ( Oku 1998 , S. Kim 1999 , Saito 2007 , Sakamoto 2015 ), (b) VP-ellipsis after verb raising ( Otani and Whitman 1991 , Funakoshi 2016 ), or (c) instances of base-generated pro ( Park 1997 , Hoji 1998 , 2003 ). We report results from two experiments supporting the argument ellipsis analysis for Korean. Experiment 1 builds on K.-M. Kim and Han’s (2016) finding of interspeaker variation in whether the pronoun ku can be bound by a quantifier. Results showed that a speaker’s acceptance of quantifier-bound ku positively correlates with acceptance of sloppy readings in NO sentences. We argue that an ellipsis account, in which the NO site contains internal structure hosting the pronoun, accounts for this correlation. Experiment 2, testing the recovery of adverbials in NO sentences, showed that only the object (not the adverb) can be recovered in the NO site, excluding the possibility of VP-ellipsis. Taken together, our findings suggest that NOs result from argument ellipsis in Korean.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.803
Threshold uncertainty score0.464

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.052
GPT teacher head0.359
Teacher spread0.306 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it