Abstract. 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.

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 a network of person interaction. In this article, we define interaction detection as a classification problem. The proposed interaction detection method, called feature‐based interactive segment recognizer (FISER), exploits 19 features covering syntactic, context‐dependent, and semantic information in text to detect intra‐clausal and inter‐clausal interactive segments in topic documents. Empirical evaluations demonstrate that FISER outperformed many well‐known relation extraction and protein–protein interaction detection methods on identifying interactive segments in topic documents. In addition, the precision, recall, and F1‐score of the best feature combination are 72.9%, 55.8%, and 63.2%, respectively.