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Visualizing Trends of Key Roles in News Articles
September 12, 2019 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Chen Xia, Haoxiang Zhang, Jacob Moghtader, Allen Wu, Kai-Wei Chang
arXiv ID
1909.05449
Category
cs.CL: Computation & Language
Citations
1
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
5 months ago
Abstract
There are tons of news articles generated every day reflecting the activities of key roles such as people, organizations and political parties. Analyzing these key roles allows us to understand the trends in news. In this paper, we present a demonstration system that visualizes the trend of key roles in news articles based on natural language processing techniques. Specifically, we apply a semantic role labeler and the dynamic word embedding technique to understand relationships between key roles in the news across different time periods and visualize the trends of key role and news topics change over time.
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