Research on Innovative Application of English Business Writing Intelligent Assisting System Based on Image Recognition Algorithm and Language Modeling
Bibliographic record
Abstract
This paper focuses on the demand for intelligent assistance in English business writing scenarios and proposes an intelligent assistance system for English business writing based on image recognition algorithm and language model.The system is able to quickly extract image information related to the writing topic through the similarity vocabulary matching technology combined with the image retrieval recognition function based on CBLSTM-Attention model.The language model is utilized to make accurate vocabulary recommendation and expression for the writing scene and user input content, and finally construct the overall framework of the intelligent assistive system based on English business writing.The system performs well in terms of vocabulary matching accuracy and writing efficiency improvement, with an average matching accuracy of over 90%.Students' quality of writing is essentially improved with the help of the system in this paper.The actual case study shows that studying under the intelligent assistance system, the post-test scores of English business composition of the students in the experimental class increased significantly by 9.9393 points (P < 0.05) compared with the average scores of the control class, and it is obvious that applying the model of this paper to the classroom teaching can lead to a significant improvement in the performance of the students, which demonstrates the good prospect of its application.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".