MétaCan
Menu
Back to cohort
Record W2032260294 · doi:10.1016/s1571-0661(05)80122-7

ASF+SDF parsing tools applied to ELAN

2000· article· en· W2032260294 on OpenAlex

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

VenueElectronic Notes in Theoretical Computer Science · 2000
Typearticle
Languageen
FieldComputer Science
TopicLogic, programming, and type systems
Canadian institutionsPrevention of Organ Failure
FundersCentrum Wiskunde and InformaticaNederlandse Organisatie voor Wetenschappelijk OnderzoekEuropean Research Consortium for Informatics and Mathematics
KeywordsParsingComputer scienceSyntaxProgramming languageRotation formalisms in three dimensionsLR parserParser combinatorAbstract syntax treeNatural language processingAbstract syntaxSyntax errorDevelopment (topology)Top-down parsingArtificial intelligenceMathematics

Abstract

fetched live from OpenAlex

This paper describes the development a new ELAN parser using ASF+SDF parsing technology. Asf+Sdf and ELAN are two modern rule-based systems. Both systems have their own features and application domains, however, both formalisms have user-defined syntax for defining rewrite rules. The Asf+Sdf Meta-Environment uses powerful and efficient generic parsing tools, whereas the ELAN parser is based on an Earley parser. Furthermore, the ELAN syntax is “hard-wired” in the parser, which makes adaptations of the syntax cumbersome. The use of Asf+Sdf parsing technology makes the ELAN syntax more open and adaptable, however, some features of the ELAN syntax makes the development of a parser a challenging problem.

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)0.002
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.940
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.000
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 head0.247
Teacher spread0.236 · 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