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Record W2156176983 · doi:10.1109/icci-cc.2013.6622221

A semantic algebra for cognitive linguistics and cognitive computing

2013· article· en· W2156176983 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicCognitive Computing and Networks
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceComputational semanticsCognitive linguisticsNatural language processingCognitive semanticsSemantic compressionCognitive computingSemantic computingArtificial intelligenceSemantics (computer science)Programming languageLinguisticsOperational semanticsCognitionSemantic technologySemantic WebPsychology

Abstract

fetched live from OpenAlex

Semantics is the meaning of a language unit at the levels of word, phrase, sentence, paragraph, and essay. Cognitive linguistics focuses on cognitive semantics of sentences and its interaction with syntactic structures. A denotational mathematical framework of language semantics known as semantic algebra is developed in this paper. Semantic algebra reveals the nature of semantics by a general mathematical model. On the basis of the formal semantic structure, language semantics can be deductively manipulated by a set of algebraic operations at different levels of language units. According to semantic algebra, semantic interpretation and comprehension can be embodied as a process of formal semantic aggregation in cognitive linguistics from the bottom up. Applications of semantic algebra are illustrated in computational linguistics, computing with words, cognitive informatics, and cognitive computing.

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)0.000
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.967
Threshold uncertainty score0.771

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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

Quick stats

Citations14
Published2013
Admission routes1
Has abstractyes

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