MétaCan
Menu
Back to cohort
Record W1588233381

Coordination of Standard Arabic Subject Markers: Implementing the Agreement Asymmetries in the ACCG Framework

2010· article· en· W1588233381 on OpenAlexaff
Ismaïl Biskri, Louisette Emirkanian, Adel Jebali

Bibliographic record

VenueThe Florida AI Research Society · 2010
Typearticle
Languageen
FieldComputer Science
TopicNatural Language Processing Techniques
Canadian institutionsUniversité du Québec à MontréalUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsSubject (documents)AgreementObject (grammar)Computer scienceLinguisticsArabicDiscourse markerModern Standard ArabicNatural language processingGrammarArtificial intelligencePhilosophyWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

In Standard Arabic, object markers and subject markers behave differently, although they share some properties. We are concerned here by their morphosyntactic status; whether they are arguments or agreement markers. The status of object markers is not an issue by itself, but the status of subject markers is one. The agreement asymmetries lead us to stipulate that, when subject markers are doubled by NPs whether these NPs are coordinated or not, they are agreement markers and not arguments. This analysis is implemented by means of an applicative combinatory categorial grammar (ACCG).

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0030.003
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.032
GPT teacher head0.385
Teacher spread0.353 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueThe Florida AI Research SocietySame topicNatural Language Processing TechniquesFrench-language works237,207