SPIRIT: A tree kernel-based method for topic person interaction detection (Extended abstract)

Yung Chun Chang, Chien Chin Chen, Wen Lian Hsu

研究成果: 書貢獻/報告類型會議貢獻

摘要

In this paper, we investigate the interactions between topic persons to help readers construct the background knowledge of a topic. We proposed a rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a treebased convolution kernel to identify segments that convey person interactions and further construct person interaction networks. Empirical evaluations demonstrate that the proposed method is effective in detecting and extracting the interactions between topic persons in the text, and outperforms other extraction approaches used for comparison. Furthermore, readers will be able to easily navigate through the topic persons of interest within the interaction networks, and further construct the background knowledge of the topic to facilitate comprehension.
原文英語
主出版物標題Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
發行者IEEE Computer Society
頁面13-14
頁數2
ISBN(電子)9781509065431
DOIs
出版狀態已發佈 - 五月 16 2017
對外發佈Yes
事件33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, 美国
持續時間: 四月 19 2017四月 22 2017

會議

會議33rd IEEE International Conference on Data Engineering, ICDE 2017
國家美国
城市San Diego
期間4/19/174/22/17

    指紋

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

引用此

Chang, Y. C., Chen, C. C., & Hsu, W. L. (2017). SPIRIT: A tree kernel-based method for topic person interaction detection (Extended abstract). 於 Proceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017 (頁 13-14). [7929909] IEEE Computer Society. https://doi.org/10.1109/ICDE.2017.13