Using the Web as a Linguistic Resource to Automatically Correct Lexico-Syntactic Errors
Bibliographic record
Abstract
This paper presents an algorithm for correcting language errors typical of second-language learners.We focus on preposition errors, which are very common among second-language learners but are not addressed well by current commercial grammar correctors and editing aids.The algorithm takes as input a sentence containing a preposition error (and possibly other errors as well), and outputs the correct preposition for that particular sentence context.We use a two-phase hybrid rule-based and statistical approach.In the first phase, rulebased processing is used to generate a short expression that captures the context of use of the preposition in the input sentence.In the second phase, Web searches are used to evaluate the frequency of this expression, when alternative prepositions are used instead of the original one.We tested this algorithm on a corpus of 133 French sentences written by intermediate second-language learners, and found that it could address 69.9% of those cases.In contrast, we found that the best French grammar and spell checker currently on the market, Antidote, addressed only 3% of those cases.We also showed that performance degrades gracefully when using a corpus of frequent ngrams to evaluate frequencies.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".