Building a reference lexicon for countability in English
Bibliographic record
Abstract
The present paper describes the construction of a resource to determine the lexical preference class of a large number of English nouns (≈ 14,000) with respect to the distinction between mass and count interpretations.In constructing the lexicon, we have employed a questionnaire-based approach based on existing resources such as the Open ANC (http://www.anc.org) and WordNet (Miller, 1995).The questionnaire requires annotators to answer six questions about a noun-sense pair.Depending on the answers, a given noun-sense pair can be assigned to fine-grained noun classes, spanning the area between count and mass.The reference lexicon contains almost 14,000 noun-sense pairs.An initial data set of 1,000 has been annotated together by four native speakers, while the remaining 12,800 noun-sense pairs have been annotated in parallel by two annotators each.We can confirm the general feasibility of the approach by reporting satisfactory values between 0.694 and 0.755 in inter-annotator agreement using Krippendorff's α.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.014 |
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".