@article{oai:nagasaki-u.repo.nii.ac.jp:00016257,
 author = {Masada, Tomonari and Takasu, Atsuhiro and Hamada, Tsuyoshi and Shibata, Yuichiro and Oguri, Kiyoshi},
 journal = {Lecture Notes in Computer Science},
 month = {May},
 note = {In this paper, we propose a new probabilistic model, Bag of Timestamps (BoT), for chronological text mining. BoT is an extension of latent Dirichlet allocation (LDA), and has two remarkable features when compared with a previously proposed Topics over Time (ToT), which is also an extension of LDA. First, we can avoid overfitting to temporal data, because temporal data are modeled in a Bayesian manner similar to word frequencies. Second, BoT has a conditional probability where no functions requiring time-consuming computations appear. The experiments using newswire documents show that BoT achieves more moderate fitting to temporal data in shorter execution time than ToT., Advances in Data and Web Management. Joint International Conferences, APWeb/WAIM 2009 Suzhou, China, April 2-4, 2009 Proceedings, Lecture Notes in Computer Science, 5446, pp.556-561; 2009},
 pages = {556--561},
 title = {Bag of Timestamps: A Simple and Efficient Bayesian Chronological Mining},
 volume = {5446},
 year = {2009}
}