Degrees of Separation in Semantic and Syntactic Relationships
Bibliographic record
Abstract
Computational models of distributional semantics can ana-lyze a corpus to derive representations of word meanings interms of each word’s relationship to all other words in the cor-pus. While these models are sensitive to topic (e.g., tiger andstripes) and synonymy (e.g., soar and fly), the models havelimited sensitivity to part of speech (e.g., book and shirt areboth nouns). By augmenting a holographic model of semanticmemory with additional levels of representations, we presentevidence that sensitivity to syntax is supported by exploitingassociations between words at varying degrees of separation.We find that sensitivity to associations at three degrees of sep-aration reinforces the relationships between words that sharepart-of-speech and improves the ability of the model to con-struct grammatical sentences. Our model provides evidencethat semantics and syntax exist on a continuum and emergefrom a unitary cognitive system.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".