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

Yung Chun Chang, Chien Chin Chen, Wen Lian Hsu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE 33rd International Conference on Data Engineering, ICDE 2017
PublisherIEEE Computer Society
Pages13-14
Number of pages2
ISBN (Electronic)9781509065431
DOIs
Publication statusPublished - May 16 2017
Externally publishedYes
Event33rd IEEE International Conference on Data Engineering, ICDE 2017 - San Diego, United States
Duration: Apr 19 2017Apr 22 2017

Conference

Conference33rd IEEE International Conference on Data Engineering, ICDE 2017
CountryUnited States
CitySan Diego
Period4/19/174/22/17

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ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems

Cite this

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