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.

本文探究了如何利用 Wikipidia 概念传递查询上下文以提高对嘈杂文本的主动信息检索的性能，并提出了两种模型，利用实体链接将 Wikipedia 主题与相关性模型关联。实验结果表明，Wikipedia 概念与检索结果相关性之间存在明显信号，关联排序模型的使用可以进一步提高精度，并且使用 Wikipedia 处理查询背景语境可以消除歧义，进一步帮助主动信息检索。