Extracting Synonyms from Dictionary Definitions
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Bibliographic record
Abstract
We investigate the problem of extracting synonyms from dictionary definitions. Our premise for using def-inition texts in dictionaries is that, in contrast to free-texts, their composition usually exhibits more regular-ities in terms of syntax and style and thus, will pro-vide a better controlled environment for synonym ex-traction. We propose three extraction methods: two rule-based ones and one using the maximum entropy model; each method is evaluated on three experiments — by solving TOEFL synonym questions, by compar-ing extraction results with existing thesauri, and by la-beling synonyms in definition texts. Results show that simple rule-based extraction methods perform surpris-ingly well on solving TOEFL synonym questions; they actually out-perform the best reported lexicon-based method by a large margin, although they do not corre-late as well with existing thesauri.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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