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Record W2042774692 · doi:10.1016/j.jides.2015.02.003

A typed applicative system for a language and text processing engineering

2014· article· en· W2042774692 on OpenAlexafffund
Ismaïl Biskri, Marie Anastacio, Adam Joly, Boucif Amar Bensaber

Bibliographic record

VenueJournal of Innovation in Digital Ecosystems · 2014
Typearticle
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceModular designProgramming languageSet (abstract data type)Consistency (knowledge bases)Text processingRule-based machine translationChain (unit)Natural language processingArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper, we present a flexible, modular, consistent, and coherent approach for language and text processing engineering. Each processing chain dedicated to text processing is regarded as a serial or parallel assembly of modules, underlying particular tasks a user wants to apply to a text. Users, according to their needs and perspectives might want to build and validate their own processing chain by assembling a set of modules according to a certain configuration. In this paper, we suggest a theoretical formal system based on the model of the typed applicative grammars and the combinatory logic. This approach allows providing a general framework in which users would be able to build multiple language and text analysis processes according to their own objectives. It will also systematize the verification of the logical consistency of the sequence of modules in the assembly that characterizes a given processing chain.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.910
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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 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

Citations1
Published2014
Admission routes2
Has abstractyes

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