FISER: An Effective Method for Detecting Interactions between Topic Persons

Discovering the interactions between the persons mentioned in a set of topic documents can help readers construct the background of the topic and facilitate document comprehension. To discover person interactions, we need a detection method that can identify text segments containing information about the interactions. Information extraction algorithms then analyze the segments to extract interaction tuples and construct an interaction network of topic persons. In this paper, we define interaction detection as a classification problem. The proposed interaction detection method, called FISER, exploits nineteen features covering syntactic, context-dependent, and semantic information in text to detect interactive segments in topic documents. Empirical evaluations demonstrate the efficacy of FISER, and show that it significantly outperforms many well-known Open IE methods.