Identifying Time Intervals of Interest to Queries

We investigate how time intervals of interest to a query can be identified automatically based on pseudo-relevant docu-ments, taking into account both their publication dates and temporal expressions from their contents. Our approach is based on a generative model and is able to determine time in-tervals at di↵erent temporal granularities (e.g., day, month, or year). We evaluate our approach on twenty years ’ worth of newspaper articles from The New York Times using two novel testbeds consisting of temporally unambiguous and temporally ambiguous queries, respectively.