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Record W2278923730 · doi:10.3986/jz.v17i2.2379

Razvoj algoritma za samodejno prepoznavanje krajšav in krajšavnih razvezav v elektronskih besedilih

2015· article· sl· W2278923730 on OpenAlexfundno aff
Mojca Kompara

Bibliographic record

VenueJezikoslovni zapiski · 2015
Typearticle
Languagesl
FieldComputer Science
TopicFuzzy Logic and Control Systems
Canadian institutionsnot available
FundersSimon Fraser UniversityUniversity of Waikato
KeywordsMathematicsPhysics

Abstract

fetched live from OpenAlex

Namen prispevka je predstaviti razvoj algoritma za samodejno prepoznavanje krajšav in krajšavnih razvezav v slovenskih elektronskih besedilih. Prepoznavanje krajšav poteka na leksikalni oz. besedni ravni z opazovanjem lastnosti krajšav in krajšavnih razvezav ter sovpadanja. Algoritem prepozna krajšave na podlagi pravil za prepoznavanje, razvezave pa išče v sobesedilu ob upoštevanju pravil sovpadanja. V prispevku predstavljamo algoritem na podlagi filtriranja petih letnikov dnevnika Delo, s katerim v 30 minutah izluščimo 5820 kandidatov za krajšavno-razvezavne pare, ki so potem ročno čiščeni. Natančnost algoritma je 96,75-odstotna.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.730
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0050.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.003

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 head0.062
GPT teacher head0.268
Teacher spread0.206 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2015
Admission routes1
Has abstractyes

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