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Record W3045694578 · doi:10.5334/gjgl.852

Processing ambiguities in attachment and pronominal reference

2020· article· en· W3045694578 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

VenueGlossa a journal of general linguistics · 2020
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsAmbiguitySentenceSentence processingReading (process)Resolution (logic)Ambiguity resolutionComputer sciencePsychologyLinguisticsSelection (genetic algorithm)PronounNatural language processingArtificial intelligence

Abstract

fetched live from OpenAlex

The nature of ambiguity resolution has important implications for models of sentence processing in general. Studies of structural ambiguities, such as modifier attachment ambiguities, have generally supported a model in which a single analysis of ambiguous material is adopted without a cost to processing. Concurrently, a separate literature has observed a processing penalty for ambiguities in pronominal reference, suggesting that potential referents compete for selection during the processing of ambiguous pronouns. We argue that the apparent distinction between the ambiguity resolution mechanisms in attachment and pronominal reference ambiguities warrants further study. We present evidence from two experiments measuring eye movements during reading, showing that the separation held in the literature between these two ambiguity types is, at least, not uniformly supported.

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)0.002
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 score0.158
Threshold uncertainty score0.386

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.067
GPT teacher head0.317
Teacher spread0.250 · 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