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        <title>Visualizing Trends of Key Roles in News Articles</title>
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        <namePart type="given">Chen</namePart>
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        <namePart type="given">Haoxiang</namePart>
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    <name type="personal">
        <namePart type="given">Kai-Wei</namePart>
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    <abstract>There are tons of news generated every day reflecting the change of key roles such as people, organizations and political parties. Analyzing the trend of these key roles can help understand the information flow in a more effective way. In this paper, we present a demonstration system that visualizes the news trend of key roles based on natural language processing techniques. Specifically, we apply semantic role labelling to understand relationships between key roles in the news. We also train a dynamic word embedding model to align representations of words in different time periods to measure how the similarities between a key role and news topics change over time. Note: The github link to our demo jupyter notebook and screencast video is https://github.com/kasinxc/Visualizing-Trend-of-Key-Roles-in-News-Articles</abstract>
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    <identifier type="doi">10.18653/v1/D19-3042</identifier>
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        <date>2019-11</date>
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%0 Conference Proceedings
%T Visualizing Trends of Key Roles in News Articles
%A Xia, Chen
%A Zhang, Haoxiang
%A Moghtader, Jacob
%A Wu, Allen
%A Chang, Kai-Wei
%Y Padó, Sebastian
%Y Huang, Ruihong
%S Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations
%D 2019
%8 November
%I Association for Computational Linguistics
%C Hong Kong, China
%F xia-etal-2019-visualizing
%X There are tons of news generated every day reflecting the change of key roles such as people, organizations and political parties. Analyzing the trend of these key roles can help understand the information flow in a more effective way. In this paper, we present a demonstration system that visualizes the news trend of key roles based on natural language processing techniques. Specifically, we apply semantic role labelling to understand relationships between key roles in the news. We also train a dynamic word embedding model to align representations of words in different time periods to measure how the similarities between a key role and news topics change over time. Note: The github link to our demo jupyter notebook and screencast video is https://github.com/kasinxc/Visualizing-Trend-of-Key-Roles-in-News-Articles
%R 10.18653/v1/D19-3042
%U https://aclanthology.org/D19-3042
%U https://doi.org/10.18653/v1/D19-3042
%P 247-252

Markdown (Informal)

[Visualizing Trends of Key Roles in News Articles](https://aclanthology.org/D19-3042) (Xia et al., EMNLP-IJCNLP 2019)

• Visualizing Trends of Key Roles in News Articles (Xia et al., EMNLP-IJCNLP 2019)

ACL

• Chen Xia, Haoxiang Zhang, Jacob Moghtader, Allen Wu, and Kai-Wei Chang. 2019. Visualizing Trends of Key Roles in News Articles. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, pages 247–252, Hong Kong, China. Association for Computational Linguistics.