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Optimization of Intelligent Corpus and Language Writing Teaching Based on Embedded Task Processing System

 and   
Mar 24, 2025

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Figure 1.

System structure diagram
System structure diagram

Figure 2.

Corpus data processing process
Corpus data processing process

Figure 3.

Word2vec model structure
Word2vec model structure

Figure 4.

Transformer structure
Transformer structure

Figure 5.

The word embedding model based on neural network
The word embedding model based on neural network

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
Language:
English