  Extracting useful information from the user history to clearly understand
informational needs is a crucial feature of a proactive information retrieval
system. Regarding understanding information and relevance, Wikipedia can
provide the background knowledge that an intelligent system needs. This work
explores how exploiting the context of a query using Wikipedia concepts can
improve proactive information retrieval on noisy text. We formulate two models
that use entity linking to associate Wikipedia topics with the relevance model.
Our experiments around a podcast segment retrieval task demonstrate that there
is a clear signal of relevance in Wikipedia concepts while a ranking model can
improve precision by incorporating them. We also find Wikifying the background
context of a query can help disambiguate the meaning of the query, further
helping proactive information retrieval.
