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Record W3026572343 · doi:10.7557/1.9.1.5277

Restrictions on ordering of adjectives in Spanish

2020· article· en· W3026572343 on OpenAlexaff
Ana Teresa Pérez‐Leroux, Alexander Tough, Erin Pettibone, Crystal Chen

Bibliographic record

VenueBorealis – An International Journal of Hispanic Linguistics · 2020
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAdjectiveNounLinguisticsInterpretation (philosophy)Contrast (vision)Set (abstract data type)Part of speechNoun phraseComputer scienceNatural language processingArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Abstract. Sequences of multiple modifying adjectives are subject to poorly understood lexical ordering restrictions. There are certain commonalities to these restrictions across languages, as well as substantive language variation. Ordering restrictions in Spanish are still under empirical debate, with some proposing strict ordering for direct modifier adjectives; others proposing broad ordering restrictions based on the contrast between intersective and non-intersective adjectives, and yet others raising the possibility that adjectival order is fully unrestricted. The goal of the present study is to examine corpus evidence for adjectival sequences. We look at both sequences of two postnominal adjectives (Noun +Adjective + Adjective, NAA sequences) as well as sequences of one prenominal, and one postnominal adjective (Adjective + Noun +Adjective, ANA sequences). The results from the NAA datasets clearly categorically confirms that relational adjectives are structurally closer to the noun. There is some evidence for an ordering bias along the line of the intersectivity hypothesis, but little else in term of hard evidence for restrictions. Additional ordering constraints appear once we incorporate the ANA datasets into the empirical picture. One interpretation is that these restrictions can be subsumed under an approach where evaluative adjectives have to occupy the prenominal restriction. In sum, the evidence is most compatible with the middle ground approach, but not with a fully articulated set of ordering restrictions.

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.774
Threshold uncertainty score0.457

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
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 science0.0010.000
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 head0.027
GPT teacher head0.311
Teacher spread0.284 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations2
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueBorealis – An International Journal of Hispanic LinguisticsSame topicNatural Language Processing TechniquesFrench-language works237,207