Lightweight Morphology: A Methodology for Improving Text Search
Bibliographic record
Abstract
Lightweight Morphology is a new approach to morphological analysis, creating morphological variants from sets of rules. The rules are intuitive to define and at the same time, offering expressiveness and control. We defined a grammar for Lightweight Morphology. We defined how to generate English and French morphological variants using the grammar. French Language specification required 526 rules, 41 rule sets and 16,842 exception table words, while the English language specification took 123 rules, 17 rule sets and 2,589 exception table words. English and French Lightweight Morphologies were compared with two other techniques extending queries on a collection of 533 documents from the aligned Hansard of the 36th parliament (1997) of Canada. A differential recall comparison among the techniques showed that Lightweight Morphology has more queries (average of 3.9 times more) retrieving fewer irrelevant document for both English and French. The French Lightweight Morphology has more queries (average of 2.5 times more) retrieving more relevant documents. 1
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".