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A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism
Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis. Existing text clustering methods usually based on topic models which only use topic and case infomation as the global features of documents, so distinguishing between differe...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620995/ https://www.ncbi.nlm.nih.gov/pubmed/34833580 http://dx.doi.org/10.3390/s21227501 |
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author | Mao, Cunli Liang, Haoyuan Yu, Zhengtao Huang, Yuxin Guo, Junjun |
author_facet | Mao, Cunli Liang, Haoyuan Yu, Zhengtao Huang, Yuxin Guo, Junjun |
author_sort | Mao, Cunli |
collection | PubMed |
description | Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis. Existing text clustering methods usually based on topic models which only use topic and case infomation as the global features of documents, so distinguishing between different cases with similar types remains a challenge. The contents of documents contain rich local features. Taking into account the internal features of news, the information of cases and the contributions provided by different topics, we propose a clustering method of case-involved news, which combines topic network and multi-head attention mechanism. Using case information and topic information to construct a topic network, then extracting the global features by graph convolution network, thus realizing the combination of case information and topic information. At the same time, the local features are extracted by multi-head attention mechanism. Finally, the fusion of global features and local features is realized by variational auto-encoder, and the learned latent representations are used for clustering. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised clustering methods. |
format | Online Article Text |
id | pubmed-8620995 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-86209952021-11-27 A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism Mao, Cunli Liang, Haoyuan Yu, Zhengtao Huang, Yuxin Guo, Junjun Sensors (Basel) Article Finding the news of same case from the large numbers of case-involved news is an important basis for public opinion analysis. Existing text clustering methods usually based on topic models which only use topic and case infomation as the global features of documents, so distinguishing between different cases with similar types remains a challenge. The contents of documents contain rich local features. Taking into account the internal features of news, the information of cases and the contributions provided by different topics, we propose a clustering method of case-involved news, which combines topic network and multi-head attention mechanism. Using case information and topic information to construct a topic network, then extracting the global features by graph convolution network, thus realizing the combination of case information and topic information. At the same time, the local features are extracted by multi-head attention mechanism. Finally, the fusion of global features and local features is realized by variational auto-encoder, and the learned latent representations are used for clustering. The experiments show that the proposed method significantly outperforms the state-of-the-art unsupervised clustering methods. MDPI 2021-11-11 /pmc/articles/PMC8620995/ /pubmed/34833580 http://dx.doi.org/10.3390/s21227501 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Mao, Cunli Liang, Haoyuan Yu, Zhengtao Huang, Yuxin Guo, Junjun A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism |
title | A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism |
title_full | A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism |
title_fullStr | A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism |
title_full_unstemmed | A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism |
title_short | A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism |
title_sort | clustering method of case-involved news by combining topic network and multi-head attention mechanism |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8620995/ https://www.ncbi.nlm.nih.gov/pubmed/34833580 http://dx.doi.org/10.3390/s21227501 |
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