The premise of entity retrieval is to better answer search queries by returning specific entities instead of documents. Many queries mention particular entities; recognizing and linking them to the corresponding entry in a knowledge base is known as the task of entity linking in queries. In this paper we make a first attempt at bringing together these two, i.e., leveraging entity annotations of queries in the entity retrieval model. We introduce a new probabilistic component and show how it can be applied on top of any term-based entity retrieval model that can be emulated in the Markov Random Field framework, including language models, sequential dependence models, as well as their fielded variations. Using a standard entity retrieval test collection, we show that our extension brings consistent improvements over all baseline methods, includ- ing the current state-of-the-art. We further show that our extension is robust against parameter settings.
Update (16/09): Our paper received the Best Paper Honorable Mention Award at the conference. So it is definitely worth checking out ;)