The development of a topic in a set of topic documents is constituted by a series of person interactions at a specific time and place. Knowing the interactions of the persons mentioned in these documents is helpful for readers to better comprehend the documents. In this paper, we propose a topic person interaction detection method called SPIRIT, which classifies the text segments in a set of topic documents that convey person interactions. We design the rich interactive tree structure to represent syntactic, context, and semantic information of text, and this structure is incorporated into a tree-based convolution kernel to identify interactive segments. Experiment results based on real world topics demonstrate that the proposed rich interactive tree structure effectively detects the topic person interactions and that our method outperforms many well-known relation extraction and protein-protein interaction methods.
|頁（從 - 到）||2494-2507|
|期刊||IEEE Transactions on Knowledge and Data Engineering|
|出版狀態||已發佈 - 9月 1 2016|
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