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Mining Topical Communities from Linked Corpus

Author: ZhengGuoQing
Tutor: YuYong
School: Shanghai Jiaotong University
Course: Applied Computer Technology
Keywords: Participate in theme Community mining Non - parametric statistical model Hierarchical Dirichlet process
CLC: TP391.1
Type: Master's thesis
Year: 2012
Downloads: 20
Quote: 0
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Abstract


Over the last decade , mining communities in large-scale linked corpus has been a hot research topic . Link on the link corpus can be divided into two classes: first link used to represent the connection between the different documents , such as hyperlinks between blog , papers refer to each other , we will be kind of link called ' ' links between documents '' ; second link used to characterize , in the same document , different users at the same time the relationship between different users at the same time to participate in our type of link is called '' the link between users ' '. Generally speaking, in the corpus of a link constituted by the inter-user , per one document contains one or more users . The corpus data constitute a link between the user Examples include e - mail files , research papers cooperative network of relationships . Constitute the corpus data link between the document Community excavation work has achieved great success , but in the community on the link between users corpus data dug mining method or not the use of the text of the document , or the user and text content oversimplified assumptions . In this article, assigned to different users to participate in document subject variables , we propose a community for the link between users corpus data mining methods , the method uses a hierarchical Dirichlet process (Hierachical Dirichlet Process) theme variables allocation, and the model introduces automatically determine the capacity of the community number . Conference papers on cooperation between network data corpus data and a press release by the New York Times on the experimental comparison , the proposed model with two options compared to the baseline model , can more effectively extract the community structure , the extracted community structure and give a reasonable semantic interpretation .

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing
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