A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English Texts
The accuracy of traditional topic models may be compromised due to the sparsity of co-occurring vocabulary in the corpus, whereas conventional word embedding models tend to excessively prioritize contextual semantic information and inadequately capture domain-specific features in the text. This pape...
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MDPI AG
2024-11-01
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| Online Access: | https://www.mdpi.com/2078-2489/15/11/708 |
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| author | Zan Qiu Guimin Huang Xingguo Qin Yabing Wang Jiahao Wang Ya Zhou |
| author_facet | Zan Qiu Guimin Huang Xingguo Qin Yabing Wang Jiahao Wang Ya Zhou |
| author_sort | Zan Qiu |
| collection | DOAJ |
| description | The accuracy of traditional topic models may be compromised due to the sparsity of co-occurring vocabulary in the corpus, whereas conventional word embedding models tend to excessively prioritize contextual semantic information and inadequately capture domain-specific features in the text. This paper proposes a hybrid semantic representation method that combines a topic model that integrates conceptual knowledge with a weighted word embedding model. Specifically, we construct a topic model incorporating the Probase concept knowledge base to perform topic clustering and obtain topic semantic representation. Additionally, we design a weighted word embedding model to enhance the contextual semantic information representation of the text. The feature-based information fusion model is employed to integrate the two textual representations and generate a hybrid semantic representation. The hybrid semantic representation model proposed in this study was evaluated based on various English composition test sets. The findings demonstrate that the model presented in this paper exhibits superior accuracy and practical value compared to existing text representation methods. |
| format | Article |
| id | doaj-art-5f18c2ca41b4478dbe4ea8e3046a0e84 |
| institution | OA Journals |
| issn | 2078-2489 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Information |
| spelling | doaj-art-5f18c2ca41b4478dbe4ea8e3046a0e842025-08-20T02:04:52ZengMDPI AGInformation2078-24892024-11-01151170810.3390/info15110708A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English TextsZan Qiu0Guimin Huang1Xingguo Qin2Yabing Wang3Jiahao Wang4Ya Zhou5Guangxi Key Laboratory of Image and Graphic Intelligent Processing, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaGuangxi Key Laboratory of Image and Graphic Intelligent Processing, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaGuangxi Key Laboratory of Image and Graphic Intelligent Processing, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaGuangxi Key Laboratory of Image and Graphic Intelligent Processing, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaGuangxi Key Laboratory of Image and Graphic Intelligent Processing, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaGuangxi Key Laboratory of Image and Graphic Intelligent Processing, School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, ChinaThe accuracy of traditional topic models may be compromised due to the sparsity of co-occurring vocabulary in the corpus, whereas conventional word embedding models tend to excessively prioritize contextual semantic information and inadequately capture domain-specific features in the text. This paper proposes a hybrid semantic representation method that combines a topic model that integrates conceptual knowledge with a weighted word embedding model. Specifically, we construct a topic model incorporating the Probase concept knowledge base to perform topic clustering and obtain topic semantic representation. Additionally, we design a weighted word embedding model to enhance the contextual semantic information representation of the text. The feature-based information fusion model is employed to integrate the two textual representations and generate a hybrid semantic representation. The hybrid semantic representation model proposed in this study was evaluated based on various English composition test sets. The findings demonstrate that the model presented in this paper exhibits superior accuracy and practical value compared to existing text representation methods.https://www.mdpi.com/2078-2489/15/11/708text representationconceptual knowledgeword embeddingsinformation fusion |
| spellingShingle | Zan Qiu Guimin Huang Xingguo Qin Yabing Wang Jiahao Wang Ya Zhou A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English Texts Information text representation conceptual knowledge word embeddings information fusion |
| title | A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English Texts |
| title_full | A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English Texts |
| title_fullStr | A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English Texts |
| title_full_unstemmed | A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English Texts |
| title_short | A Hybrid Semantic Representation Method Based on Fusion Conceptual Knowledge and Weighted Word Embeddings for English Texts |
| title_sort | hybrid semantic representation method based on fusion conceptual knowledge and weighted word embeddings for english texts |
| topic | text representation conceptual knowledge word embeddings information fusion |
| url | https://www.mdpi.com/2078-2489/15/11/708 |
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