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Prosody and the production of ambiguous relative clauses in French

2008· article· en· W2103928343 on OpenAlexaboutno aff
Amanda Edmonds, Audrey Liljestrand Fultz, Jason Killam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicPhonetics and Phonology Research
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)AmbiguitySentenceLinguisticsSet (abstract data type)Noun phraseComputer scienceProsodyNatural language processingInterpretation (philosophy)Task (project management)PhraseNounRelative clauseArtificial intelligencePsychologySpeech recognitionHistoryPhilosophyEngineering

Abstract

fetched live from OpenAlex

When we hear the sentence he respects the butcher of the doctor who gains weight each year, it is not clear whether the appreciated butcher has put on weight or whether it is the doctor who is a bit heavier. Without additional information, the attachment of the relative clause (RC) is ambiguous, a case of structural ambiguity that is found in many languages, including English and French. Although such phrases are usually disambiguated by context, it has been shown that speakers and listeners can disambiguate several structural ambiguities by prosodic means Yet, this body of literature has concentrated almost exclusively on the ambiguity resolution of a small set of structures in English. The current study examines the prosodic strategies of final lengthening and F0 rise used to disambiguate the attachment of a RC to a complex noun phrase (NP) as employed by three native speakers (NS) of both Hexagonal and Quebecois French. Participants completed two tasks, one in which the intended interpretation of the RC was indicated through context and the other a more explicit minimal pairs task. Almost all participants employ a similar pattern of final lengthening to differentiate between the two interpretations of the RC, whereas results from F0 rise are mixed, with several patterns emerging.

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.

How this classification was reachedexpand

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.387
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.048
GPT teacher head0.336
Teacher spread0.288 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations28
Published2008
Admission routes1
Has abstractyes

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Same topicPhonetics and Phonology ResearchFrench-language works237,207