Adaptive text correction with Web-crawled domain-dependent dictionaries
Bibliographic record
Abstract
For the success of lexical text correction, high coverage of the underlying background dictionary is crucial. Still, most correction tools are built on top of static dictionaries that represent fixed collections of expressions of a given language. When treating texts from specific domains and areas, often a significant part of the vocabulary is missed. In this situation, both automated and interactive correction systems produce suboptimal results. In this article, we describe strategies for crawling Web pages that fit the thematic domain of the given input text. Special filtering techniques are introduced to avoid pages with many orthographic errors. Collecting the vocabulary of filtered pages that meet the vocabulary of the input text, dynamic dictionaries of modest size are obtained that reach excellent coverage values. A tool has been developed that automatically crawls dictionaries in the indicated way. Our correction experiments with crawled dictionaries, which address English and German document collections from a variety of thematic fields, show that with these dictionaries even the error rate of highly accurate texts can be reduced, using completely automated correction methods. For interactive text correction, more sensible candidate sets for correcting erroneous words are obtained and the manual effort is reduced in a significant way. To complete this picture, we study the effect when using word trigram models for correction. Again, trigram models from crawled corpora outperform those obtained from static corpora.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".