Cross-Lingual Topic Alignment in Time Series Japanese / Chinese News
Shuo Hu, Yusuke Takahashi, Liyi Zheng, Takehito Utsuro, Masaharu Yoshioka, Noriko Kando, Tomohiro Fukuhara, Hiroshi Nakagawa, Yoji Kiyota · Institutional Repositories DataBase (IRDB) · 2012
Among various types of recent information explosion, that in news stream is also a kind of serious problems.This paper studies issues regarding topic modeling of information flow in multilingual news streams.If someone wants to find differences in the topics of Japanese news and Chinese news, it is usually necessary for him/her to carefully watch every article in Japanese and Chinese news streams at every moment.In such a situation, topic models such as LDA (Latent Dirichlet Allocation) and DTM (dynamic topic model) are quite effective in estimating distribution of topics over a document collection such as articles in a news stream.Especially, as a topic model, this paper employs DTM, but not LDA, since it can consider correspondence between topics of consecutive dates.Based on the results of estimating distribution of topics in Japanese / Chinese news streams, this paper proposes how to analyze cross-lingual alignment of topics in time series Japanese / Chinese news streams.