Hybrid Approach for Automated Answer Scoring Using Semantic Analysis in Long Hindi Text
Bibliographic record
Abstract
In the case of the Hindi language, the technology that underpins automated scoring is still in its infancy in terms of its development.These systems have shown much better accuracy and reliability in their operations Nowadays, several studies are being carried out in addition to saving individuals money and time.with the intention of providing nuanced feedback on grammatical as well as semantic problems.This paper's main objective is to develop a hybrid methodology for automated answer scoring using semantic analysis for long Hindi text.Deep Learning and Recurrent Neural Network method have been taken into consideration throughout this research study.The ability of recurrent neural networks, to learn the temporal dependency of sequential input gives them an edge over feed forward neural networks, when it comes to the scoring of musical responses.Research work has integrated PSO and Roberta to improve accuracy.Based on the research findings, the recommended approach has been shown to outperform the currently recognized revolutionary techniques.It shows that the Hybrid PSO-Roberta based deep learning strategy performs better than the old system in terms of precision, recall, and f1 score.It reduce the amount of paperwork they need to do, teachers won't have to be concerned about any evaluation issues going away either.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".