Truecasing for the Portage system
Bibliographic record
Abstract
This paper presents a truecasing technique- that is, a technique for restoring the normal case form to an all lowercased or partially cased text. The technique uses a combination of statistical components, including an N-gram language model, a case mapping model, and a specialized language model for unknown words. The system is also capable of distinguishing between “title ” and “non-title ” lines, and can apply different statistical models to each type of line. The system was trained on the data taken from the English portion of the Canadian parliamentary Hansard corpus and on some English-language texts taken from a corpus of China-related stories; it was tested on a separate set of texts from the China-related corpus. The system achieved 96 % case accuracy when the China-related test corpus had been completely lowercased; this represents 80 % relative error rate reduction over the unigram baseline technique. Subsequently, our technique was implemented as a module called Portage-Truecasing inside a machine translation system called Portage, and its effect on the overall performance of Portage was tested. In this paper, we explore the truecasing concept, and then we explain the models used. 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.013 |
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".