—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, content, and semantic information in the text for extracting person interactions. Subsequently, a model-based EM method is employed to discover the stance communities of the topic persons to assist the exhibition of the 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.
—Topic summarization, interaction extraction, person multi-polarization, stance community identification, topic person interaction network.
Y.-C. Chang, Z.-Y. Chen, and C.-C. Chen are with the Department of Information Management, National Taiwan University, Taiwan (e-mail: firstname.lastname@example.org, email@example.com, firstname.lastname@example.org).
W. L. Hsu is with the Institute of Information Science, Academia Sinica, Taiwan (e-mail: email@example.com).
Cite:Yung-Chun Chang, Zhong-Yong Chen, Chien-Chin Chen, and Wen-Lian Hsu, "Constructing Topic Person Interaction Networks Using a Tree Kernel-Based Method," International Journal of Languages, Literature and Linguistics vol. 1, no. 4, pp. 238-245, 2015.