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Record W1974722138 · doi:10.1121/1.4784172

Function words of lexical bundles: The relation of frequency and reduction.

2009· article· en· W1974722138 on OpenAlexaff
S Lemke, Antoine Tremblay, Benjamin V. Tucker

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2009
Typearticle
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPredictabilityReduction (mathematics)Word (group theory)Computer scienceWord lists by frequencySpeech recognitionSpeech productionFunction (biology)Duration (music)MathematicsLinguisticsNatural language processingAcousticsStatisticsPhysicsBiology

Abstract

fetched live from OpenAlex

Studies of spontaneous speech have shown frequency effects on the amount of reduction produced by speakers, demonstrating that predictability facilitates production of a target word [Shi et al. (2005); Jurafsky et al. (2001); Bell et al. (2003)]. This paper investigates the amount of reduction produced in laboratory recorded speech and considers the effect of frequency on the duration of function words in four-word sequences. It is also found that the influence of frequency has an effect on holistically storing these bundles. An interaction between word position and the third-order transitional probability (ABC → D) has been established, indicating that greater third-order transitional probabilities predict shorter function word durations in the first and second positions of a bundle, and, therefore, involve more durational reduction. The current research shows that, just as frequency affects reduction in spontaneous speech, there is an effect in laboratory produced speech as well. These findings indicate that multiword sequences are stored as lexical units.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.920
Threshold uncertainty score0.192

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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 designBench or experimental
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

Citations1
Published2009
Admission routes1
Has abstractyes

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