@techreport{oai:ipsj.ixsq.nii.ac.jp:00057136,
 author = {越仲, 孝文 and 磯, 健一 and 奥村, 明俊 and Takafumi, Koshinaka and Ken-Ichi, Iso and Akitoshi, Okumura},
 issue = {57(2004-SLP-051)},
 month = {May},
 note = {確率モデルに基づくテキスト分割法を提案する．left-to-right型の離散HMMをテキスト生成モデルと考え，テキスト分割をHMMのパラメータ推定問題として定式化する．パラメータ推定法として，最尤推定およびベイズ推定（変分ベイズ法）を用いて，日本語ニュース番組を各ニュース項目へ分割する評価実験を行い，最尤推定に比べてペイズ推定が精度よくテキストを分割できることを示す．さらに，従来法としてHearst法を取り上げ，従来法と比べた提案法の利点や課題を明らかにする．, This paper presents a new text segmentation method based on stochastic modeling. When supposing a generative model of a text document to be a discrete left-to-right hidden Markov model (HMM), a transition between topics in the text document corresponds to a state transition in the HMM, and text segmentation can be formulated as model parameter estimation using the text document. Compared to the traditional maximum likelihood approach, advantage of the Bayes approach (Variational Bayes) is shown by some experiments, which evaluate segmentation accuracy in segmenting Japanese broadcast news programs into each news article. Comparison between the proposed method and a conventional method, well-known Hearst's method, is also presented in this paper. The comparison shows the proposed method to be encouraging.},
 title = {HMMの変分ベイズ学習によるテキスト文書の話題分割法},
 year = {2004}
}