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。

基于维基百科概念的噪音文本主动信息检索

Towards Proactive Information Retrieval in Noisy Text with Wikipedia Concepts

We describe a method for proactive information retrieval targeted at
retrieving relevant information during a writing task. In our method, the
current task and the needs of the user are estimated, and the potential next
steps are unobtrusively predicted based on the user's past actions. We focus on
the task of writing, in which the user is coalescing previously collected
information into a text. Our proactive system automatically recommends the user
relevant background information. The proposed system incorporates text input
prediction using a long short-term memory (LSTM) network. We present
simulations, which show that the system is able to reach higher precision
values in an exploratory search setting compared to both a baseline and a
comparison system.

本研究通过使用 LSTM 网络和背景信息推荐系统，为写作任务提供积极信息检索，以提高搜索质量。