Ranking events based on user relevant query

Given a collection of event-related documents, event ranking generates a list of ranked events based on the input query. Ranking news events, which takes event related news documents for the generation of ranked events, is both an essential research issue and important component for many security oriented applications, such as public event monitoring, retrieval, detection and mining. Previous related work solely relies on queries of event relevant aspects, and user relevant aspects of queries that are critical for security applications are totally ignored. In this paper, we deal with the problem of news ranking by incorporating user relevant information into the input query, from the cluster of relevant new documents and comments. Given an input query, which contains event related objective aspects(e.g. actors, locations, date) and user related subjective aspects(e.g. public attention and opinion polarity), we develop a Learning-to-Rank framework to integrate aspect-level correlation between query and event. Experiments on a crawled large news corpus show the effectiveness of our proposed approach compared to several baseline models.