Abstract
Nowadays, with the development of the Internet, large amount of continuous streaming news has become overwhelming to the public. Constructing a dynamic topic hierarchy which organizes the news articles according to multi-grain topics can enable the users to catch whatever they are interested in as soon as possible. However, it is nontrivial due to the streaming and time-sensitive characteristics of news data. In this paper, to address the challenges, we propose a Hierarchical Entity Topic Model (HETM) which considers the timeliness of news data and the importance of named entities in conveying information of who/when/where in news articles. In addition, we propose online HETM (o-HETM) by presenting a fast online inference algorithm for HETM to adapt it to streaming news. For better understanding of topics, we extract key sentences for each topic to form a summary. Extensive experimental results demonstrate that our model HETM significantly improves the topic quality and time efficiency, compared to state-of-the-art method HLDA (Hierarchical Latent Dirichlet Allocation). In addition, our proposed o-HETM with an online inference algorithm further greatly improves the time efficiency and thus can be applicable to the streaming news.
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Hu, L., Li, J., Zhang, J., Shao, C. (2015). o-HETM: An Online Hierarchical Entity Topic Model for News Streams. In: Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D., Motoda, H. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2015. Lecture Notes in Computer Science(), vol 9077. Springer, Cham. https://doi.org/10.1007/978-3-319-18038-0_54
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DOI: https://doi.org/10.1007/978-3-319-18038-0_54
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