Constructing Topic Person Interaction Networks Using a Tree Kernel-Based Method

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.
Index Terms—Topic summarization, interaction extraction,
person multi-polarization, stance community identification,
topic person interaction network.