Event-Centered Information Retrieval Using Kernels on Event Graphs

Traditional information retrieval models assume keyword-based queries and use unstructured document representations. There is an abundance of event-centered texts (e.g., breaking news) and event-oriented information needs that often involve structure that cannot be expressed using keywords. We present a novel retrieval model that uses a structured event-based representation. We structure queries and documents as graphs of event mentions and employ graph kernels to measure the query-document similarity. Experimental results on two event-oriented test collections show significant improvements over state-ofthe-art keyword-based models.