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A Knowledge-Based Semisupervised Hierarchical Online Topic Detection Framework
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DOI：10.1109/TCYB.2018.2841504.png)
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摘要
En 中文
Topic models have achieved big success in recent years. To detect topics in a text stream, various online topic models have been proposed in the literature. The limitations of these works include that: 1) most of them run with fixed topic numbers and 2) the overlaps between the topics may enlarge in the evolving process. Hierarchical topic model is a candidate solution to these problems since it can reveal many useful relationships between the topics. These relationships can help to find high quality topics and reduce topic overlaps. In this paper, a knowledge-based semisupervised hierarchical online topic detection framework is proposed. The proposed framework can detect topics in an online hierarchical way. In addition, it has been proven that introducing external knowledge can improve the performance of text mining. Therefore, the knowledge from external knowledge sources and human experts are also integrated in the proposed framework. Experiments are conducted to evaluate the proposed framework with different metrics. The results show that compared with the baseline methods, our framework can achieve better performance with competitive time efficiency.
Keyword:
Distance metric learning
hierarchical matrix factorization
online topic model
text semantic relatedness
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期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
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