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• W2317288149 abstract "This research suggests news sentence network analysis algorithm based on similarity and cooccurence. News contains abundant arguments with facts and quotes those are critical to represent agendas. News sentence network is a semantic network which consists of quotes as nodes. Connectivity is defined by relevance between quotes. Relevance matrix is the sum of similarity matrix calculated by cosine similarity algorithm and cooccurence matrix. This study analyzed 949 quotes from 405 news articles and visualized networks. The results verified that semantic paths were well defined to show the sequence of sub-agendas. News semantic network analysis algorithm can provide a methodology to automatically generate a massive corpus in a sentence level as a training set to develop a debating machine." @default.
• W2317288149 created "2016-06-24" @default.
• W2317288149 creator A5059578037 @default.
• W2317288149 date "2016-01-27" @default.
• W2317288149 modified "2023-10-17" @default.
• W2317288149 title "Toward a debating machine: A news sentence network analysis algorithm based on similarity and cooccurrence" @default.
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• W2317288149 doi "https://doi.org/10.17210/hcik.2016.01.491" @default.
• W2317288149 hasPublicationYear "2016" @default.
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