Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis
How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues...
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| Published in: | Computational intelligence and neuroscience Vol. 2022; pp. 1 - 8 |
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| Format: | Journal Article |
| Language: | English |
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United States
Hindawi
28.03.2022
John Wiley & Sons, Inc |
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| ISSN: | 1687-5265, 1687-5273, 1687-5273 |
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| Abstract | How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain “outliers,” and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person’s understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation. |
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| AbstractList | How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain “outliers,” and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person's understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation. How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain "outliers," and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person's understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation.How to facilitate users to quickly and accurately search for the text information they need is a current research hotspot. Text clustering can improve the efficiency of information search and is an effective text retrieval method. Keyword extraction and cluster center point selection are key issues in text clustering research. Common keyword extraction algorithms can be divided into three categories: semantic-based algorithms, machine learning-based algorithms, and statistical model-based algorithms. There are three common methods for selecting cluster centers: randomly selecting the initial cluster center point, manually specifying the cluster center point, and selecting the cluster center point according to the similarity between the points to be clustered. The randomly selected initial cluster center points may contain "outliers," and the clustering results are locally optimal. Manually specifying the cluster center points will be very subjective because each person's understanding of the text set is different, and it is not suitable for the case of a large number of text sets. Selecting the cluster center points according to the similarity between the points to be clustered can make the selected cluster center points distributed in each class and be as close as possible to the class center points, but it takes a long time to calculate the cluster centers. Aiming at this problem, this paper proposes a keyword extraction algorithm based on cluster analysis. The results show that the algorithm does not rely on background knowledge bases, dictionaries, etc., and obtains statistical parameters and builds models through training. Experiments show that the keyword extraction algorithm has high accuracy and can quickly extract the subject content of an English translation. |
| Audience | Academic |
| Author | Ma, Jingxia |
| AuthorAffiliation | School of Western Languages and Cultures, Harbin Normal University, Harbin 150025, China |
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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35387240$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.1371/journal.pone.0245259 10.1080/19397038.2020.1866708 10.1109/TSMC.2020.3043016 10.1016/j.ijinfomgt.2019.01.021 10.1007/978-3-030-12939-2_3 10.1016/j.ssci.2020.104705 10.3390/robotics8040090 10.1145/564376.564483 10.1146/annurev-neuro-080317-062007 10.37547/jcass/volume01issue01-a6 10.3115/1119355.1119383 10.1016/j.jclepro.2018.11.270 10.3390/e20020104 10.3390/s20020333 10.5120/19161-0607 10.1109/ICRA.2014.6907456 10.1001/jama.2019.3785 10.3390/electronics10070828 10.1088/1742-6596/1278/1/012018 10.1007/s40171-021-00277-7 10.1007/s10551-019-04204-w 10.1109/TFUZZ.2020.3002431 10.1007/11775300_8 10.1146/annurev-environ-012320-085130 10.1002/14651858.CD006732.pub4 10.1007/978-3-540-77046-6_62 10.1016/j.eswa.2017.12.025 10.1056/nejmra1801063 10.3115/1613172.1613178 10.1109/auteee52864.2021.9668824 |
| ContentType | Journal Article |
| Copyright | Copyright © 2022 Jingxia Ma. COPYRIGHT 2022 John Wiley & Sons, Inc. Copyright © 2022 Jingxia Ma. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2022 Jingxia Ma. 2022 |
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| References | 22 24 25 26 27 C. Zhang (2) 2008; 4 28 M. Yang (13) 29 E. van Dijk (36) F. Xiao (9) 2020; 29 30 31 10 32 11 Y. Wang (23) 2019; 13 33 34 E. Herrera-Viedma (35) 2020; 51 37 16 17 18 J. Tebbe (12) 19 J. Kay (14) 2020 J. Kaur (1) 2010; 7 3 4 5 G. K. Palshikar (6) A W J Marchau Vincent (7) 2019 8 Y. Zhang (15) 20 21 37538722 - Comput Intell Neurosci. 2023 Jul 26;2023:9791307 |
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| Title | Research on Keyword Extraction Algorithm in English Text Based on Cluster Analysis |
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