A Cognitive Machine Learning System for Phrases Composition and Semantic Comprehension
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.
Bibliographic record
Abstract
Although lexical and syntactic theories for phrase analyses have been well studied in linguistic theories and computational linguistics, semantic synthesis theories for cognitive computing are still a challenge in machine learning and brain-inspired systems. This paper studies theories and mathematical models of machine knowledge learning and semantic comprehension. An Algorithm of Unsupervised Phrase Learning (AUPL) is developed that enables cognitive machines to autonomously learn phrase semantics in the sixth category of machine knowledge learning. A set of experimental results is reported to demonstrate the methodology and algorithm. This work plays a fundamental role for sentence learning where the semantics of natural languages is reduced onto those of phrases and terminal words represented by formal concepts in cognitive systems.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it