Event-based cross media question answering

User generated content, available in massive amounts on the Internet, is receiving increased attention due to its many potential applications. One of such applications is the representation of events using multimedia data. In this paper, an event-based cross media question answering system, which retrieves and summarizes events on a given topic is proposed. In orther words, we present a framework for leveraging social media data to extract and illustrate social events automatically on any given query. The system is built in three steps. First, the input query is parsed semantically to identify the topic, location, and time information related to the News of interest. Then, we use the parsed information to mine the latest and hottest related News

from social news web services. Third, to identify a unique event, we model the News content by latent Dirichlet Allocation and cluster the News using the DBSCAN algorithm. In the end, for each event, we retrieve both textual and visual content of News that refer the same event. The resulting documents are shown within a vivid interface featuring both event description,

tag cloud and photo collage.