Optimization of Intelligent Corpus and Language Writing Teaching Based on Embedded Task Processing System
and
Mar 24, 2025
About this article
Published Online: Mar 24, 2025
Received: Oct 31, 2024
Accepted: Feb 16, 2025
DOI: https://doi.org/10.2478/amns-2025-0771
Keywords
© 2025 Yan Li et al., published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
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Comparison of Chinese writing level of 2 groups before the experiment
| Dimension | Experimental group | Control group | t | p |
|---|---|---|---|---|
| M±SD | M±SD | |||
| Accuracy of words | 6.01±1.49 | 5.98±1.36 | 0.645 | 0.825 |
| Discourse fluency | 5.93±1.75 | 5.95±1.83 | -0.284 | 0.776 |
| Article structure | 5.15±1.45 | 5.27±1.63 | -0.438 | 0.794 |
| Textual logic | 5.72±1.97 | 5.88±1.55 | -0.305 | 0.771 |
| Stylized narrative | 5.25±1.31 | 5.04±1.38 | 0.296 | 0.868 |
| Writing standards | 5.34±1.33 | 5.22±1.82 | 0.542 | 0.872 |
| Subject innovation | 6.06±1.72 | 5.82±1.52 | 0.687 | 0.705 |
| Linguistic beauty | 5.44±1.60 | 5.46±1.85 | -0.152 | 0.844 |
| Explicit resolution | 5.42±1.33 | 5.65±1.68 | -0.723 | 0.914 |
| Total | 50.32±7.88 | 50.27±8.27 | 0.315 | 0.888 |
Comparison of Chinese writing level of experimental group before and after experiment
| Dimension | Before | After | t | p |
|---|---|---|---|---|
| M±SD | M±SD | |||
| Accuracy of words | 6.01±1.49 | 9.78±5.13 | 3.312 | 0.003 |
| Discourse fluency | 5.93±1.75 | 9.76±5.62 | 3.548 | 0.003 |
| Article structure | 5.15±1.45 | 9.48±5.61 | 4.152 | 0.002 |
| Textual logic | 5.72±1.97 | 10.46±3.93 | 4.928 | 0.002 |
| Stylized narrative | 5.25±1.31 | 10.22±4.44 | 5.516 | 0.001 |
| Writing standards | 5.34±1.33 | 9.82±4.60 | 4.625 | 0.002 |
| Subject innovation | 6.06±1.72 | 9.24±5.53 | 2.584 | 0.004 |
| Linguistic beauty | 5.44±1.60 | 9.66±4.38 | 3.954 | 0.003 |
| Explicit resolution | 5.42±1.33 | 10.18±4.40 | 5.035 | 0.001 |
| Total | 50.32±7.88 | 88.60±13.52 | 15.554 | 0.000 |
Comparison of Chinese writing level of control group before and after experiment
| Dimension | Before | After | t | p |
|---|---|---|---|---|
| M±SD | M±SD | |||
| Accuracy of words | 5.98±1.36 | 6.95±1.34 | 1.654 | 0.734 |
| Discourse fluency | 5.95±1.83 | 6.24±1.62 | 0.521 | 0.905 |
| Article structure | 5.27±1.63 | 6.04±1.26 | 0.985 | 0.594 |
| Textual logic | 5.88±1.55 | 6.47±1.73 | 0.745 | 0.703 |
| Stylized narrative | 5.04±1.38 | 5.92±1.64 | 1.035 | 0.685 |
| Writing standards | 5.22±1.82 | 5.65±1.26 | 0.634 | 0.763 |
| Subject innovation | 5.82±1.52 | 6.02±1.46 | 0.312 | 0.671 |
| Linguistic beauty | 5.46±1.85 | 5.89±1.31 | 0.642 | 0.721 |
| Explicit resolution | 5.65±1.68 | 5.83±1.99 | 0.242 | 0.584 |
| Total | 50.27±8.27 | 55.01±7.26 | 3.685 | 0.807 |
Comparison of Chinese writing level of 2 groups after the experiment
| Dimension | Experimental group | Control group | t | p |
|---|---|---|---|---|
| M±SD | M±SD | |||
| Accuracy of words | 9.78±5.13 | 6.95±1.34 | 3.984 | 0.004 |
| Discourse fluency | 9.76±5.62 | 6.24±1.62 | 4.956 | 0.003 |
| Article structure | 9.48±5.61 | 6.04±1.26 | 4.738 | 0.003 |
| Textual logic | 10.46±3.93 | 6.47±1.73 | 5.744 | 0.002 |
| Stylized narrative | 10.22±4.44 | 5.92±1.64 | 6.029 | 0.001 |
| Writing standards | 9.82±4.60 | 5.65±1.26 | 5.035 | 0.002 |
| Subject innovation | 9.24±5.53 | 6.02±1.46 | 4.356 | 0.003 |
| Linguistic beauty | 9.66±4.38 | 5.89±1.31 | 4.967 | 0.003 |
| Explicit resolution | 10.18±4.40 | 5.83±1.99 | 6.464 | 0.001 |
| Total | 88.60±13.52 | 55.01±7.26 | 14.979 | 0.000 |
The writing dataset text classification experiment results
| Text representation method | Accuracy | Precision | Recall | F1 | Rank |
|---|---|---|---|---|---|
| DTM | 0.683 | 0.687 | 0.737 | 0.870 | 5 |
| FBOW | 0.787 | 0.863 | 0.878 | 0.886 | 2 |
| LDA | 0.811 | 0.726 | 0.701 | 0.881 | 3 |
| Word2Vec-DTM | 0.749 | 0.882 | 0.880 | 0.635 | 12 |
| P-SIF | 0.847 | 0.794 | 0.822 | 0.733 | 7 |
| Doc2Vec | 0.699 | 0.731 | 0.651 | 0.674 | 11 |
| WME | 0.748 | 0.839 | 0.813 | 0.875 | 4 |
| TextGCN | 0.697 | 0.723 | 0.761 | 0.728 | 8 |
| Attention-BiLSTM | 0.716 | 0.793 | 0.700 | 0.726 | 9 |
| TextCNN | 0.870 | 0.776 | 0.723 | 0.691 | 10 |
| XLNet | 0.842 | 0.770 | 0.880 | 0.850 | 6 |
| Ours | 0.908 | 0.920 | 0.907 | 0.896 | 1 |
