Unsupervised multi-author document decomposition based on hidden Markov model

This paper proposes an unsupervised approach for segmenting a multiauthor document into authorial components. The key novelty is that we utilize the sequential patterns hidden among document elements when determining their authorships. For this purpose, we adopt Hidden Markov Model (HMM) and construct a sequential probabilistic model to capture the dependencies of sequential sentences and their authorships. An unsupervised learning method is developed to initialize the HMM parameters. Experimental results on benchmark datasets have demonstrated the significant benefit of our idea and our approach has outperformed the state-of-the-arts on all tests. As an example of its applications, the proposed approach is applied for attributing authorship of a document and has also shown promising results.