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TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction
When TextRank algorithm based on graph model constructs graph associative edges, the co-occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808205/ https://www.ncbi.nlm.nih.gov/pubmed/35126495 http://dx.doi.org/10.1155/2022/5649994 |
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author | Zhou, Ning Shi, Wenqian Liang, Renyu Zhong, Na |
author_facet | Zhou, Ning Shi, Wenqian Liang, Renyu Zhong, Na |
author_sort | Zhou, Ning |
collection | PubMed |
description | When TextRank algorithm based on graph model constructs graph associative edges, the co-occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction algorithm based on rough data reasoning combined with word vector clustering, RDD-WRank, was proposed. Firstly, the algorithm uses rough data reasoning to mine the association between candidate keywords, expands the search scope, and makes the results more comprehensive. Then, based on Wikipedia online open knowledge base, word embedding technology is used to integrate Word2Vec into the improved algorithm, and the word vector of TextRank lexical graph nodes is clustered to adjust the voting importance of nodes in the cluster. Compared with the traditional TextRank algorithm and the Word2Vec algorithm combined with TextRank, the experimental results show that the improved algorithm has significantly improved the extraction accuracy, which proves that the idea of using rough data reasoning can effectively improve the performance of the algorithm to extract keywords. |
format | Online Article Text |
id | pubmed-8808205 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-88082052022-02-03 TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction Zhou, Ning Shi, Wenqian Liang, Renyu Zhong, Na Comput Intell Neurosci Research Article When TextRank algorithm based on graph model constructs graph associative edges, the co-occurrence window rules only consider the relationships between local terms. Using the information in the document itself is limited. In order to solve the above problems, an improved TextRank keyword extraction algorithm based on rough data reasoning combined with word vector clustering, RDD-WRank, was proposed. Firstly, the algorithm uses rough data reasoning to mine the association between candidate keywords, expands the search scope, and makes the results more comprehensive. Then, based on Wikipedia online open knowledge base, word embedding technology is used to integrate Word2Vec into the improved algorithm, and the word vector of TextRank lexical graph nodes is clustered to adjust the voting importance of nodes in the cluster. Compared with the traditional TextRank algorithm and the Word2Vec algorithm combined with TextRank, the experimental results show that the improved algorithm has significantly improved the extraction accuracy, which proves that the idea of using rough data reasoning can effectively improve the performance of the algorithm to extract keywords. Hindawi 2022-01-25 /pmc/articles/PMC8808205/ /pubmed/35126495 http://dx.doi.org/10.1155/2022/5649994 Text en Copyright © 2022 Ning Zhou et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Zhou, Ning Shi, Wenqian Liang, Renyu Zhong, Na TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction |
title | TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction |
title_full | TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction |
title_fullStr | TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction |
title_full_unstemmed | TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction |
title_short | TextRank Keyword Extraction Algorithm Using Word Vector Clustering Based on Rough Data-Deduction |
title_sort | textrank keyword extraction algorithm using word vector clustering based on rough data-deduction |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8808205/ https://www.ncbi.nlm.nih.gov/pubmed/35126495 http://dx.doi.org/10.1155/2022/5649994 |
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