The Use of Approximate String Matching Techniques in the Alignment of Sentences in Parallel Corpora Abstract
Bibliographic record
Abstract
Parallel corpora such as the Canadian Hansard corpus and the International Telecommunications Union (ITU) corpus each provide the same text in two or more languages, and have been aptly described as the "Rosetta Stone " of modern corpus linguistics [1]. Their use within MT is burgeoning, permeating all levels of the discipline, and even being used as the basis of full-blown statistically based MT systems. This paper will concern itself with the task of automatic bilingual lexicon construction, which is one of the major goals of the CRATER project (“Corpus Resources and Terminology Extraction”, funded under the MLAP initiative of the CEC, grant number MLAP-93/20). The approach to bilingual lexicon alignment taken here entails the alignment of corpora, and then a detailed search through the corpus for lexical cognates. Consequently the paper will begin with a brief discussion of the alignment procedures used on the project to date, and move to a discussion of various similarity metrics used to evaluate lexical similarity. 1 Introduction: Language independent and language-pair specific approaches to alignment Parallel Corpora provide an ideal test-bed for many tasks, such as translation tuition and the production of probabilistic dictionaries. To be of use, however, they must first be aligned, so that it is known which segments in one corpus correspond with which segments in the other. Current
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".