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Record W2783006698 · doi:10.3765/salt.v27i0.4144

Ambiguous than-clauses and the mention-some reading

2017· article· en· W2783006698 on OpenAlex
Linmin Zhang, Jia Ling

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

VenueProceedings from Semantics and Linguistic Theory · 2017
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsConcordia University
Fundersnot available
KeywordsAmbiguitySentenceNegationContext (archaeology)Computer scienceReading (process)Semantics (computer science)LinguisticsOperator (biology)Meaning (existential)ModalArithmeticMathematicsNatural language processingProgramming languagePhilosophy

Abstract

fetched live from OpenAlex

This paper addresses the ambiguity of comparatives that contain a permission-related existential modal in their than-clause. For example, given the context that the interval of permitted speed is between 35 and 50 mph, the sentence Lucinda is driving less fast than allowed is ambiguous between two readings: (i) her speed is below the minimum (i.e., 35 mph); (ii) her speed is below the maximum (i.e., 50 mph). Previously, this ambiguity has been attributed to either the scopal interaction between a negation element and a modal (Heim 2006a) or the optional application of a silent operator (Crnic 2017). Here we show that these two lines of accounts under- or over-generate. Instead, we propose that the source of this ambiguity is located in the ambiguous answerhood for wh-questions corresponding to this kind of than-clauses (e.g., how fast is Lucinda allowed to drive). The current proposal consists of three parts. First, based on Zhang & Ling (2015, 2017a,b), we adopt a generalized interval-arithmetic-based recipe for computing the semantics of comparatives. Second, the semantics of than-clauses is considered equal to that of short answers to corresponding wh-questions. Third, since the use of existential priority modals in wh-questions leads to the ‘mention-some/mention-all’ ambiguity for answerhood, we propose that this ambiguity projects in further derivation and leads to the two readings for comparatives like the Lucinda sentence.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.259
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.000
Open science0.0010.001
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 head0.009
GPT teacher head0.253
Teacher spread0.244 · 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