Hierarchical online NMF for detecting and tracking topic hierarchies in a text stream
Hierarchical online NMF for detecting and tracking topic hierarchies in a text stream
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用于检测和跟踪文本流中的主题层次结构的分层在线 NMF
DOI：
10.1016/j.patcog.2017.11.002
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发表时间：
2018-04
影响因子：
8
通讯作者：
Gencai Chen
中科院分区：
文献类型：
--
作者：
Ding Tu;Ling Chen;Mingqi Lv;Hongyu Shi;Gencai Chen
Discovering and tracking topics in a text stream has attracted the interests of many researchers. A limitation of most existing methods is that they organize topics in flat structures. Topic hierarchy could reveal the potential relations between topics, which can help to find high quality topics when analyzing the text stream. In this paper, a hierarchical online non-negative matrix factorization method (HONMF) is proposed to generate topic hierarchies from text streams. The proposed method can dynamically adjust the topic hierarchy to adapt to the emerging, evolving, and fading processes of the topics. In the experiment, HONMF is evaluated under a variety of metrics. Compared with the baseline methods, our method can achieve better performance with competitive time efficiency.
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DOI：
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发表时间：
2007-01
期刊：
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影响因子：
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DOI：
10.1198/016214506000000302
发表时间：
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影响因子：
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DOI：
10.1109/taslp.2013.2285474
发表时间：
2014
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IEEE/ACM Transactions on Audio, Speech, and Language Processing
影响因子：
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