FISER: A Feature-Based Detection System for Person Interactions

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.