澳大利亚悉尼科技大学何祥健教授学术报告 9月12日上午
发布时间: 2017-09-11 访问次数: 57

报告人:何祥健

  

报告题目:SUDMAD: Sequential and Unsupervised Decomposition of a Multi-Author Document Based on a Hidden Markov Model

  

时间:2017-09-12 (星期二) 09:00 ~ 2017-09-12 (星期二) 10:00

  

地点:成功楼603报告厅

  

主办:数学与信息学院, 福建省网络安全与密码技术重点实验室

  

参加对象:相关研究方向老师和研究生

  

报告摘要: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 rooms 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 to topics, through the construction of a Hidden Markov Model (HMM) reflecting 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 authors or documents context other than how many authors have 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 labelled 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 which are widely used for authorship analysis of documents. Comparisons with recent state-the-art approaches are also presented to demonstrate the significance of our new ideas and the superior performance of our approach.

  

报告人简介:Professor Xiangjian He is the Director of Computer Vision and Pattern Recognition Laboratory at the Global Big Data Technologies Centre (GBDTC), at the University of Technology, Sydney (UTS). He is the Director of UTS-NPU International Joint Laboratory on Digital Media and Intelligent Networks. He is an IEEE Senior Member and has been an IEEE Signal Processing Society Student Committee member. He has been awarded 'Internationally Registered Technology Specialist' by International Technology Institute (ITI). He has been carrying out research mainly in