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Record W4405764804 · doi:10.1080/23273798.2024.2443975

Underspecified <i>they</i> becomes specified early in sentence processing

2024· article· en· W4405764804 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLanguage Cognition and Neuroscience · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsUniversity of TorontoUniversity of British ColumbiaUniversity of CalgarySimon Fraser University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsComputer scienceSentence processingNatural language processingSentenceUnderspecificationSpeech recognitionLinguisticsArtificial intelligence

Abstract

fetched live from OpenAlex

We investigated underspecification in sentence processing, using the ambiguous pronoun they as a case study. We asked whether they is processed as underspecified for number, and probed into when it acquires its number specification in incremental processing. A key question we address is whether underspecification is due to the fact that the pronoun is lexically underspecified for number or due to shallow processing. Based on the findings from an acceptability judgment study and two reading time studies using the Maze task, we conclude that they is lexically underspecified for number and that the processor homes in on a more enriched specification of they early, soon after it links with its antecedent and before fully disambiguating material is encountered. The data suggest that readers will develop detailed commitments to semantic representations for ambiguous expressions early in processing. We discuss how this finding stands in contrast to underspecified plural definite descriptions.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.050
GPT teacher head0.310
Teacher spread0.261 · 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