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.
|名字||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|會議||8th Asia Information Retrieval Societies Conference, AIRS 2012|
|期間||12/17/12 → 12/19/12|