Visualizing Trend of Key Roles in News Articles

Chen Xia, Haoxiang Zhang, Jacob Moghtader, Allen Wu, and Kai-Wei Chang, in EMNLP (demo), 2019.

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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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Bib Entry

@inproceedings{xia2019visualizing,
  author = {Xia, Chen and Zhang, Haoxiang and Moghtader, Jacob and Wu, Allen and Chang, Kai-Wei},
  title = {Visualizing Trend of Key Roles in News Articles},
  booktitle = {EMNLP (demo)},
  year = {2019}
}