TAER: time-aware entity retrieval-exploiting the past to find relevant entities in news articles

Retrieving entities instead of just documents has become an important task for search engines. In this paper we study entity retrieval for news applications, and in particular the importance of the news trail history (i.e., past related articles) in determining the relevant entities in current articles. This is an important problem in applications that display retrieved entities to the user, together with the news article. We analyze and discuss some statistics about entities in news trails, unveiling some unknown findings such as the persistence of relevance over time. We focus on the task of query dependent entity retrieval over time. For this task we evaluate several features, and show that their combinations significantly improves performance.