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The Effects of Intelligent Semantic Analysis Techniques on Language Acquisition in the Improvement of English Intercultural Communication Skills

Xiangming Huang

Xiangming Huang

School of Management, Guangdong Ocean UniversityZhanjiang, China
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Huang, Xiangming
  
17 mars 2025
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Publié en ligne: 17 mars 2025
Reçu: 17 nov. 2024
Accepté: 18 févr. 2025
DOI: https://doi.org/10.2478/amns-2025-0264
Mots clés
© 2025 Xiangming Huang, published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Figure 1.

Semantic analysis of the intelligent algorithm structure
Semantic analysis of the intelligent algorithm structure

Figure 2.

Representation learning of word embedding
Representation learning of word embedding

Figure 3.

LSTM inner structure
LSTM inner structure

Figure 4.

The semantic content of the sentence is accurate
The semantic content of the sentence is accurate

Figure 5.

The semantic component of the question is marked by the recall rate
The semantic component of the question is marked by the recall rate

Figure 6.

The semantic component of the question is marked f1
The semantic component of the question is marked f1

Quantity statistics

Semantic component Training language Test language Semantic component Training language Test language
Characteristic number Forecast number Characteristic number Forecast number Characteristic number Forecast number Characteristic number Forecast number
Tie 7981 6621 256 242 Degree 567 531 19 15
Consul 26 25 1 1 Range 4632 3765 134 108
Suffer 25 13 1 0 Trend 1999 1891 71 69
Results 821 710 26 23 Cause 30 27 1 1
Guest 5632 4982 155 141 Purpose 26 26 1 1
And things 172 132 5 4 Predicate 7255 7168 256 242
Party 24 11 0 0 Marker 5176 5133 186 185
Tools 246 231 7 6 Unit 156 156 6 6
Mode 322 315 10 9 yw 79 67 2 2
Space 5834 5521 198 182 ots 3978 3786 135 126
Time 166 121 5 4 - - - - -

Error analysis of Chinese sentence collection

Analyser AMR Mate Parser Malt Parser
Syntax type Grammatical type Lexical error Dependent error Microscope Lexical error Dependent error Microscope Lexical error Dependent error Microscope
Phrase structure Modification relation 25 78 162 27 145 214 121 111 311
Functional relation 11 48 1 41 29 50
Sentence structure Component relation 42 185 358 69 197 462 72 218 430
Small sentence relation 46 85 41 155 45 165
Other Undefined dependencies 4 22 26 12 23 35 14 31 45
Total 128 418 546 150 561 711 281 575 786

The student school language USES regression analysis

Variable assignment Regression coefficient Standard error Z value Significance
Use and do not use
Seniors will use no =0
yes = 1 .06429 .4183 0.13 0.996
Inclarity =2 -.9618 1.0717 -0.81 0.289
Lower grades will use no =0
yes = 1 -.3649 .3916 0.9 0.371
Inclarity =2 1.7742 0.9853 1.77 0.002
Teachers don’t use it yes=0 .7065 .2382 2.93 0.023
Gender Female=0 -.5794 .2332 2.45 0.033
Age -.4206 .0892 -4.66 0
Constant term 5.4251 1.255 4.29 0
Both are involved (use and not use) VS Not use
Seniors will use no=0
yes=1 -.2769 0.3868 -0.69 0.593
Inclarity = 2 .0109 .9797 0.02 0.997
Lower grades will use no=0
yes=1 .3011 .3652 0.8 0.396
Inclarity =2 .9651 .9485 1.06 0.314
Teachers don’t use it yes=0 .2249 .2293 0.95 0.341
Gender female=0 -.3751 .2248 -1.63 0.095
Age -.1554 0.0877 -1.74 0.082
Constant term 2.638 1.235 2.11 0.153
N 578
LRchi2 59.58
Prob>chi2 0.0000
Log likeihood -588.6723
Pseudo R2 0.0402

The accuracy of the syntax analysis tool is calculated

Analyser Language Style Dependent relation Whole sentence
Mini Max Average
AMR English Literature 15.88% 100% 87.56% 41%
Inliterature 37.28% 100% 86.91% 34%
Chinese Literature 11.4% 100% 81.42% 32%
Inliterature 32.55% 100% 84.51% 23%
Mate Parser English Literature 27.12% 100% 86.31% 28%
Inliterature 13.21% 100% 85.33% 21%
Chinese Literature 10.2% 100% 77.51% 16%
Inliterature 45% 100% 80.55% 7%
Malt Parser English Literature 13.21% 100% 80.78% 28%
Inliterature 40% 100% 81.21% 20%
Chinese Literature 11.4% 100% 76.57% 17%
Inliterature 41.51% 100% 78.32% 6%

Error analysis of English sentence collection

Analyser AMR Mate Parser Malt Parser
Syntax type Grammatical type Lexical error Dependent error Microscope Grammatical type Lexical error Dependent error Grammatical type Lexical error Dependent error
Phrase structure Modification relation 15 127 182 26 145 218 27 199 284
Functional relation 5 35 9 38 2 56
Sentence structure Component relation 8 120 261 19 151 331 18 227 467
Small sentence relation 9 124 18 143 13 209
Other Undefined dependencies 1 7 8 1 15 16 1 11 12
Total 38 413 451 73 492 565 61 702 763
Langue:
Anglais
Périodicité:
1 fois par an
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