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Record W2250555305

On Panini and the Generative Capacity of Contextualized Replacement Systems

2012· article· en· W2250555305 on OpenAlex
Gerald Penn, Paul Kiparsky

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

VenueInternational Conference on Computational Linguistics · 2012
Typearticle
Languageen
FieldComputer Science
Topicsemigroups and automata theory
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGenerative grammarFormalism (music)SanskritComputer scienceGrammarRewritingLinguisticsMildly context-sensitive grammar formalismAdaptive grammarEmergent grammarNatural language processingProgramming languageMathematicsArtificial intelligencePhilosophyLiterature
DOInot available

Abstract

fetched live from OpenAlex

This paper re-examines the widely held belief that the formalism underlying the rule system propounded by the ancient Indian grammarian, Pān. ini (ca. 450–350 BCE), either anticipates or converges upon the same expressive power found in finite state control systems or the context-free languages that are used in programming language theory and computational linguistics. While there is indeed a striking but cosmetic resemblance to the contextualized rewriting systems used by modern morphologists and phonologists, a subtle difference in how rules are prevented from applying cyclically leads to a massive difference in generative capacity. The formalism behind Pān. inian grammar, in fact, generates string languages not even contained within any of the multiple-component tree-adjoining languages, MCTAL(k), for any k. There is ample evidence, nevertheless, that Pān. ini’s grammar itself judiciously avoided the potential pitfalls of this unconstrained formalism to articulate a large-coverage, but seemingly very tractable grammar of the Sanskrit language.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.928
Threshold uncertainty score0.360

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.058
GPT teacher head0.304
Teacher spread0.246 · 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