IWF-TextRank Keyword Extraction Algorithm Modelling
Keywords are used to provide a concise summary of the text, enabling the quick understanding of core information and assisting in filtering out irrelevant content. In this paper, an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank) is pro...
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10657 |
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| author | Liyan Zhang Wenhui Wang Jian Ma Yuan Wen |
| author_facet | Liyan Zhang Wenhui Wang Jian Ma Yuan Wen |
| author_sort | Liyan Zhang |
| collection | DOAJ |
| description | Keywords are used to provide a concise summary of the text, enabling the quick understanding of core information and assisting in filtering out irrelevant content. In this paper, an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank) is proposed to improve the accuracy of keyword extraction by comprehensively considering multiple features of words. The key innovation is demonstrated through the application of a backpropagation neural network, combined with sequential relationship analysis, to calculate the comprehensive weight of words. Additionally, word vectors trained using Word2Vec are utilised to enhance the model’s semantic understanding. Finally, the effectiveness of the algorithm is verified from various aspects using traffic accident causation data. The results show that this algorithm demonstrates a significant optimisation effect in keyword extraction. Compared with the traditional model, the IWF-TextRank algorithm shows significant improvement in accuracy (<i>p</i>-value), recall (R-value), and F-value. |
| format | Article |
| id | doaj-art-3b7782f46774485bb46d17bc348d09cd |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3b7782f46774485bb46d17bc348d09cd2025-08-20T02:26:47ZengMDPI AGApplied Sciences2076-34172024-11-0114221065710.3390/app142210657IWF-TextRank Keyword Extraction Algorithm ModellingLiyan Zhang0Wenhui Wang1Jian Ma2Yuan Wen3School of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, ChinaSchool of Business, Suzhou University of Science and Technology, Suzhou 215009, ChinaSchool of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, ChinaSchool of Civil Engineering, Suzhou University of Science and Technology, Suzhou 215011, ChinaKeywords are used to provide a concise summary of the text, enabling the quick understanding of core information and assisting in filtering out irrelevant content. In this paper, an improved TextRank keyword extraction algorithm based on word vectors and multi-feature weighting (IWF-TextRank) is proposed to improve the accuracy of keyword extraction by comprehensively considering multiple features of words. The key innovation is demonstrated through the application of a backpropagation neural network, combined with sequential relationship analysis, to calculate the comprehensive weight of words. Additionally, word vectors trained using Word2Vec are utilised to enhance the model’s semantic understanding. Finally, the effectiveness of the algorithm is verified from various aspects using traffic accident causation data. The results show that this algorithm demonstrates a significant optimisation effect in keyword extraction. Compared with the traditional model, the IWF-TextRank algorithm shows significant improvement in accuracy (<i>p</i>-value), recall (R-value), and F-value.https://www.mdpi.com/2076-3417/14/22/10657TextRankkeyword extractionword vectorsmultivariate feature weighting |
| spellingShingle | Liyan Zhang Wenhui Wang Jian Ma Yuan Wen IWF-TextRank Keyword Extraction Algorithm Modelling Applied Sciences TextRank keyword extraction word vectors multivariate feature weighting |
| title | IWF-TextRank Keyword Extraction Algorithm Modelling |
| title_full | IWF-TextRank Keyword Extraction Algorithm Modelling |
| title_fullStr | IWF-TextRank Keyword Extraction Algorithm Modelling |
| title_full_unstemmed | IWF-TextRank Keyword Extraction Algorithm Modelling |
| title_short | IWF-TextRank Keyword Extraction Algorithm Modelling |
| title_sort | iwf textrank keyword extraction algorithm modelling |
| topic | TextRank keyword extraction word vectors multivariate feature weighting |
| url | https://www.mdpi.com/2076-3417/14/22/10657 |
| work_keys_str_mv | AT liyanzhang iwftextrankkeywordextractionalgorithmmodelling AT wenhuiwang iwftextrankkeywordextractionalgorithmmodelling AT jianma iwftextrankkeywordextractionalgorithmmodelling AT yuanwen iwftextrankkeywordextractionalgorithmmodelling |