Reconciling Fine-Grained Lexical Knowledge and Coarse-Grained Ontologies in the Representation of Near-Synonyms
Bibliographic record
Abstract
dimension seep : drip Emphasis enemy : foe Denotational, indirect error : blunder Denotational, fuzzy woods : forest Table 1: Examples of near-synonymic variation. discrimination dictionaries, Edmonds (1999) gives a classification of near-synonymic variation into 35 subcategories of the above four broad categories. Table 1 gives several examples, which we will discuss briefly. Collocational variation involves the words or concepts with which a word can be combined, possibly idiomatically, in a well-formed sentence. For example, task and job differ in their collocational patterns: face a daunting job sounds unnatural where face a daunting task does not. Stylistic variation involves differences in a small finite set of dimensions on which all words can be compared. Many stylistic dimensions have been proposed by Hovy (1988), Nirenburg & Defrise (1992), Stede (1993), and others. Table 1 illustrates two of the most common dimensions: Inebriated is formal while pissed is informal; ann...
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How this classification was reachedexpand
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) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".