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...

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Autores principales:	Cunli Mao, Haoyuan Liang, Zhengtao Yu, Yuxin Huang, Junjun Guo
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	topic clustering graph convolution network multi-head attention mechanism global feature local feature case-involved news Chemical technology TP1-1185
Acceso en línea:	https://doaj.org/article/e4bbbc95a6a84bcdb95c9dda9f4c319a
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spelling	oai:doaj.org-article:e4bbbc95a6a84bcdb95c9dda9f4c319a2021-11-25T18:56:58ZA Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism10.3390/s212275011424-8220https://doaj.org/article/e4bbbc95a6a84bcdb95c9dda9f4c319a2021-11-01T00:00:00Zhttps://www.mdpi.com/1424-8220/21/22/7501https://doaj.org/toc/1424-8220Finding 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.Cunli MaoHaoyuan LiangZhengtao YuYuxin HuangJunjun GuoMDPI AGarticletopic clusteringgraph convolution networkmulti-head attention mechanismglobal featurelocal featurecase-involved newsChemical technologyTP1-1185ENSensors, Vol 21, Iss 7501, p 7501 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	topic clustering graph convolution network multi-head attention mechanism global feature local feature case-involved news Chemical technology TP1-1185
spellingShingle	topic clustering graph convolution network multi-head attention mechanism global feature local feature case-involved news Chemical technology TP1-1185 Cunli Mao Haoyuan Liang Zhengtao Yu Yuxin Huang Junjun Guo A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism
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	article
author	Cunli Mao Haoyuan Liang Zhengtao Yu Yuxin Huang Junjun Guo
author_facet	Cunli Mao Haoyuan Liang Zhengtao Yu Yuxin Huang Junjun Guo
author_sort	Cunli Mao
title	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_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_sort	clustering method of case-involved news by combining topic network and multi-head attention mechanism
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/e4bbbc95a6a84bcdb95c9dda9f4c319a
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A Clustering Method of Case-Involved News by Combining Topic Network and Multi-Head Attention Mechanism

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