Exploring Vector Representations for Phonological Similarity
Bibliographic record
Abstract
Recent research has compared representation models of word meaning (Brown et al., 2023, Cognitive Science 47:e13291), however, less research has compared representation models of words’ perceptual features. Thus, we compared vector-space word representation models that can be used to quantify words’ phonological similarity. The main structure of the model was adapted from Cox et al.’s orthographic representation model (Behavior Research Methods 43:602-15, 2011). Variations of the model included phonetic mapping scheme, encoding scheme, the inclusion of lexical stress, and the combination of orthographic and phonological representations. We tested the model variants against human-rated phonological similarity and both phonological and orthographic Damerau-Levenshtein distance. Open n-gram encoding (1 ≤ n ≤ 2) performed better overall than terminal relative encoding across all phonological similarity metrics. Concatenated orthographic-phonological vectors improved the prediction of human ratings with terminal-relative encoding only. Using more fine-grained phonetic mapping or including lexical stress had minimal effects.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".