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

<p>Chinese literary texts contain a sizeable vivid imagery vocabulary, which makes it difficult for average readers to judge the boundaries between words, and the current pre-trained language model is also difficult for them to learn its implicit knowledge effectively, which brings troubles to machine semantic analysis. The study uses CRF training to obtain a semantic analysis model of Chinese literary texts that recognizes the semantic relationship between two words. SVM is used to train classifiers for confusing categories, and the two semantic relations in the output of the CRF model are further recognized to determine the final semantic relations between word pairs. Finally, the LCQMC dataset is used as the experimental data, and the semantic analysis technique based on CRF and SVM is employed to obtain the participle, lexical, and dependent syntactic annotations. According to the results, the model’s correct rates on the LAS for paraphrase recognition and dependency analysis of Chinese literary texts are 74.83% and 92.05%, respectively. The study enhances the efficiency of semantic analysis of relevant Chinese texts and is crucial for the study on the semantic analysis of terms.</p>
</abstract>

Chinese literary texts contain a sizeable vivid imagery vocabulary, which makes it difficult for average readers to judge the boundaries between words, and the current pre-trained language model is also difficult for them to learn its implicit knowledge effectively, which brings troubles to machine semantic analysis. The study uses CRF training to obtain a semantic analysis model of Chinese literary texts that recognizes the semantic relationship between two words. SVM is used to train classifiers for confusing categories, and the two semantic relations in the output of the CRF model are further recognized to determine the final semantic relations between word pairs. Finally, the LCQMC dataset is used as the experimental data, and the semantic analysis technique based on CRF and SVM is employed to obtain the participle, lexical, and dependent syntactic annotations. According to the results, the model’s correct rates on the LAS for paraphrase recognition and dependency analysis of Chinese literary texts are 74.83% and 92.05%, respectively. The study enhances the efficiency of semantic analysis of relevant Chinese texts and is crucial for the study on the semantic analysis of terms.

Imagery Recognition and Semantic Analysis Techniques in Chinese Literary Texts

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Imagery Recognition and Semantic Analysis Techniques in Chinese Literary Texts"}

Chinese literary texts contain a sizeable vivid imagery vocabulary, which makes it difficult for average readers to judge the boundaries between words, and the...

Imagery Recognition and Semantic Analysis Techniques in Chinese Literary Texts

Published Online: May 03, 2024

Page range: -

Received: Apr 11, 2024

Accepted: Apr 20, 2024

DOI: https://doi.org/10.2478/amns-2024-0950

Keywords
Semantic analysis, CRF, Dependency analysis, SVM, Chinese literary text

© 2024 Wenfu Zhang, published by Sciendo

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

Imagery Recognition and Semantic Analysis Techniques in Chinese Literary Texts

Published Online: May 03, 2024

Page range: -

Received: Apr 11, 2024

Accepted: Apr 20, 2024

DOI: https://doi.org/10.2478/amns-2024-0950

KeywordsSemantic analysis, CRF, Dependency analysis, SVM, Chinese literary text

© 2024 Wenfu Zhang, published by Sciendo

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

Keywords
Semantic analysis, CRF, Dependency analysis, SVM, Chinese literary text