<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Vocabulary is an essential and crucial part of college English learning. However, the lack of knowledge about the intrinsic connection between words has become a significant obstacle to vocabulary learning and teaching. In this study, we use a semantic association network to construct a relationship model between English words and introduce a tree kernel function into the syntactic analyzer to extract relationships between English words. The dictionary-based lexical semantic similarity calculation method is combined with a corpus-based English lexical semantic similarity calculation method for the extracted relations. Furthermore, the study creates datasets that relate to English vocabulary for simulation experiments. The results show that the more common sense phenomena there are, the higher the correlation value is. Furthermore, the similarity of university English vocabulary has a high degree of correlation with the type of specialized elements. The overall performance of the lexicon is ranked as synonym forest (0.431ρ/0.479r) > HowNet (0.249ρ/0.341r) > English WordNet (−0.051ρ/−0.003r), and the synonym forest method has the best performance, with the highest accuracy of 85.57% in classifying different lexical relations. The study offers valuable references and lessons for related research in natural language processing.</p>
</abstract>

Vocabulary is an essential and crucial part of college English learning. However, the lack of knowledge about the intrinsic connection between words has become a significant obstacle to vocabulary learning and teaching. In this study, we use a semantic association network to construct a relationship model between English words and introduce a tree kernel function into the syntactic analyzer to extract relationships between English words. The dictionary-based lexical semantic similarity calculation method is combined with a corpus-based English lexical semantic similarity calculation method for the extracted relations. Furthermore, the study creates datasets that relate to English vocabulary for simulation experiments. The results show that the more common sense phenomena there are, the higher the correlation value is. Furthermore, the similarity of university English vocabulary has a high degree of correlation with the type of specialized elements. The overall performance of the lexicon is ranked as synonym forest (0.431ρ/0.479r) > HowNet (0.249ρ/0.341r) > English WordNet (−0.051ρ/−0.003r), and the synonym forest method has the best performance, with the highest accuracy of 85.57% in classifying different lexical relations. The study offers valuable references and lessons for related research in natural language processing.

Relational Analysis of College English Vocabulary - A Reflection Based on Semantic Association Network Modeling

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Vocabulary is an essential and crucial part of college English learning. However, the lack of knowledge about the intrinsic connection between words has become...

Relational Analysis of College English Vocabulary - A Reflection Based on Semantic Association Network Modeling

Published Online: May 22, 2024

Page range: -

Received: Feb 07, 2024

Accepted: Apr 15, 2024

DOI: https://doi.org/10.2478/amns-2024-1205

Keywords
Semantic association, Relational extraction, Semantic similarity, English vocabulary

© 2024 Bojun Dai, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Relational Analysis of College English Vocabulary - A Reflection Based on Semantic Association Network Modeling

Published Online: May 22, 2024

Page range: -

Received: Feb 07, 2024

Accepted: Apr 15, 2024

DOI: https://doi.org/10.2478/amns-2024-1205

KeywordsSemantic association, Relational extraction, Semantic similarity, English vocabulary

© 2024 Bojun Dai, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Semantic association, Relational extraction, Semantic similarity, English vocabulary