SUDMAD: Sequential and unsupervised decomposition of a multi‐author document based on a hidden markov model. (29th September 2017)

• Record Type: Journal Article
• Authors: Aldebei, Khaled; He, Xiangjian; Jia, Wenjing; Yeh, Weichang
• Is Part Of: Journal of the Association for Information Science and Technology. Volume 69:Number 2(2018:Feb.)
• Page Start: 201
• Page End: 214
• Publication Date: 2017-09-29
• Subjects: Information science -- Periodicals; Information technology -- Periodicals; 020.5
• DOI: 10.1002/asi.23956
• Languages: English
• ISSNs: 2330-1635

Abstract

Abstract : Decomposing a document written by more than one author into sentences based on authorship is of great significance due to the increasing demand for plagiarism detection, forensic analysis, civil law (i.e., disputed copyright issues), and intelligence issues that involve disputed anonymous documents. Among existing studies for document decomposition, some were limited by specific languages, according to topics or restricted to a document of two authors, and their accuracies have big room for improvement. In this paper, we consider the contextual correlation hidden among sentences and propose an algorithm for Sequential and Unsupervised Decomposition of a Multi‐Author Document (SUDMAD) written in any language, disregarding topics, through the construction of a Hidden Markov Model (HMM) reflecting the authors' writing styles. To build and learn such a model, an unsupervised, statistical approach is first proposed to estimate the initial values of HMM parameters of a preliminary model, which does not require the availability of any information of author's or document's context other than how many authors contributed to writing the document. To further boost the performance of this approach, a boosted HMM learning procedure is proposed next, where the initial classification results are used to create labeled training data to learn a more accurate HMM. Moreover, the contextual relationship among sentences is further utilized to refine the classification results. Our proposed approach is empirically evaluated on three benchmark datasets that are widely used for authorship analysis of documents. Comparisons with recent state‐of‐the‐art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach.