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Record W3201804497

A Basic Morphological Parser for Discourse Information Grammar

2004· article· en· W3201804497 on OpenAlexaff
Alexandre Sévigny, James McMullan

Bibliographic record

VenuePapers from the Annual Meetings of the Atlantic Provinces Linguistic Association (PAMAPLA) · 2004
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Text Analysis Techniques
Canadian institutionsMcMaster University
Fundersnot available
KeywordsComputer sciencePerspective (graphical)ParsingLexiconGrammarMeaning (existential)Artificial intelligenceNatural language processingLinguisticsRule-based machine translationEpistemology
DOInot available

Abstract

fetched live from OpenAlex

Though many view the fundamental nature of language in terms of abstract rules applied to yield grammatical sequences, with meaning added in later, it is also possible to view language from what could be called the inverse perspective. In this perspective, the fundamental nature and purpose of language change from a rule-based approach to an information-based approach, where the fundamental purpose of language is to send, receive, store and represent information. Viewed from this perspective, grammar rules become over learned patterns and storage structures, the results of emergent properties of information accumulation and storage processes. In order to study language from this perspective, detailed attention must be paid to the lexicon: its nature, structure, content and role. This paper describes initial work undertaken toward analyzing language from an information-based approach.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.413
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
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 science0.0020.000
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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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.

Study designTheoretical or conceptual
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
Published2004
Admission routes1
Has abstractyes

Explore more

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