Lifelong Learning Augmented Short Text Stream Clustering Method
Depending on the scanning mode, existing short text stream clustering methods can be divided into the following two kinds of methods: one-pass-based and batch-based. The one-pass-based method handles each text only one time, but cannot deal with the sparseness problem very well. The batch-based meth...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9424568/ |
| _version_ | 1818479744567476224 |
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| author | Jipeng Qiang Wanyin Xu Yun Li Yunhao Yuan Yi Zhu |
| author_facet | Jipeng Qiang Wanyin Xu Yun Li Yunhao Yuan Yi Zhu |
| author_sort | Jipeng Qiang |
| collection | DOAJ |
| description | Depending on the scanning mode, existing short text stream clustering methods can be divided into the following two kinds of methods: one-pass-based and batch-based. The one-pass-based method handles each text only one time, but cannot deal with the sparseness problem very well. The batch-based method obtains better results by allowing multiple iterations of each batch, but the efficiency is relatively low. To overcome these problems, this paper presents Lifelong learning Augmented Short Text stream clustering method (LAST), which incorporates the episodic memory module and sparse experience replay module of lifelong learning into the clustering process. Specifically, LAST processes each text one time, but at a certain interval it randomly samples some previously seen texts of the episodic memory to update cluster features by performing sparse experience replay. Empirical studies on two public datasets demonstrate that the performance of the LAST-based method is on a par with the batch-based method, and runs close to the speed of the one-pass-based method. |
| first_indexed | 2024-12-10T11:14:48Z |
| format | Article |
| id | doaj.art-4d8b9618029e4bceb838bf7592e2e052 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-10T11:14:48Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4d8b9618029e4bceb838bf7592e2e0522022-12-22T01:51:14ZengIEEEIEEE Access2169-35362021-01-019704937050110.1109/ACCESS.2021.30780969424568Lifelong Learning Augmented Short Text Stream Clustering MethodJipeng Qiang0https://orcid.org/0000-0001-5721-0293Wanyin Xu1Yun Li2Yunhao Yuan3Yi Zhu4Department of Computer Science, Yangzhou University, Yangzhou, ChinaDepartment of Computer Science, Yangzhou University, Yangzhou, ChinaDepartment of Computer Science, Yangzhou University, Yangzhou, ChinaDepartment of Computer Science, Yangzhou University, Yangzhou, ChinaDepartment of Computer Science, Yangzhou University, Yangzhou, ChinaDepending on the scanning mode, existing short text stream clustering methods can be divided into the following two kinds of methods: one-pass-based and batch-based. The one-pass-based method handles each text only one time, but cannot deal with the sparseness problem very well. The batch-based method obtains better results by allowing multiple iterations of each batch, but the efficiency is relatively low. To overcome these problems, this paper presents Lifelong learning Augmented Short Text stream clustering method (LAST), which incorporates the episodic memory module and sparse experience replay module of lifelong learning into the clustering process. Specifically, LAST processes each text one time, but at a certain interval it randomly samples some previously seen texts of the episodic memory to update cluster features by performing sparse experience replay. Empirical studies on two public datasets demonstrate that the performance of the LAST-based method is on a par with the batch-based method, and runs close to the speed of the one-pass-based method.https://ieeexplore.ieee.org/document/9424568/Short text streamtext clusteringsparsenesslifelong learning |
| spellingShingle | Jipeng Qiang Wanyin Xu Yun Li Yunhao Yuan Yi Zhu Lifelong Learning Augmented Short Text Stream Clustering Method IEEE Access Short text stream text clustering sparseness lifelong learning |
| title | Lifelong Learning Augmented Short Text Stream Clustering Method |
| title_full | Lifelong Learning Augmented Short Text Stream Clustering Method |
| title_fullStr | Lifelong Learning Augmented Short Text Stream Clustering Method |
| title_full_unstemmed | Lifelong Learning Augmented Short Text Stream Clustering Method |
| title_short | Lifelong Learning Augmented Short Text Stream Clustering Method |
| title_sort | lifelong learning augmented short text stream clustering method |
| topic | Short text stream text clustering sparseness lifelong learning |
| url | https://ieeexplore.ieee.org/document/9424568/ |
| work_keys_str_mv | AT jipengqiang lifelonglearningaugmentedshorttextstreamclusteringmethod AT wanyinxu lifelonglearningaugmentedshorttextstreamclusteringmethod AT yunli lifelonglearningaugmentedshorttextstreamclusteringmethod AT yunhaoyuan lifelonglearningaugmentedshorttextstreamclusteringmethod AT yizhu lifelonglearningaugmentedshorttextstreamclusteringmethod |