Knowledge Integration in a Robust and Efficient
Bibliographic record
Abstract
Lome H. Bouchard Dtp. de mathtmatiques et d'infonnatique UniversitE du QuEbec h MontrEal C.P. 8888, Succursale "A" MontrEal, QC Canada H3C 3P8 R15320 @ UQAM. BITNET We present a morpho-syntactic analyzer for French which is capable of automatically detecting and of correcting (automatically or with user help) spelling mistakes, agreement errors and certain frequently encountered syntactic errors. Emphasizing the specific language knowledge that is used, we describe the major subtasks of this analyzer: word categorization by dictionary look-up and spelling correction, construction of a parse tree or of a forest of parse trees, correction of syntactic and mollhological errors by processing the parse tree. The spelling corrector module is designed to help correct the spelling mistakes of a French novice, as opposed to those of an experienced typist. The syntax analysis module is driven by an empirical grammar for French and is based on the work of Tomits. The presentation is based on the design and implementation of a prototype of the system which is written in Lisp for the Macintosh computer.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.012 |
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".