Imperfect Querying through Womb Grammars plus Ontologies.
Bibliographic record
Abstract
Womb grammars, or WGs, are a failure-driven constraint-based parsing mechanism specifically developed for cross-language grammar engineering, whose main parsing operation consists of looking for failed constraints between pairs of daughters of a phrasal category. For instance, rather than rejecting those noun phrases where an adjective daughter precedes the noun daughter (a natural mistake for say, an Italian querying in English), a WG checks whether that English ordering requirement fails, and produces a failure indicator if so. Thus, rather than acting solely as filters impeding incorrect sentences from being parsed, the constraints described for a WG can be relaxed to admit mistakes that are personalized to a certain type of user. Syntactic constraints have been the most studied for WGs, since their first aim was to “repair” a known language’s grammar until it reflected that of another language, by modifying constraints that failed with respect to input in the other language. However any other kind of information can also be consulted. In this article we extend WG parsing to incorporate semantic information in view of imperfect querying, and we show how the approach lends itself in particular to ontology-driven enhancements . We assume familiarity with Prolog and in particular, CHR.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".