We introduce the task of entity-centric query refinement. Given an input
query whose answer is a (potentially large) collection of entities, the task
output is a small set of query refinements meant to assist the user in
efficient domain exploration and entity discovery. We propose a method to
create a training dataset for this task. For a given input query, we use an
existing knowledge base taxonomy as a source of candidate query refinements,
and choose a final set of refinements from among these candidates using a
search procedure designed to partition the set of entities answering the input
query. We demonstrate that our approach identifies refinement sets which human
annotators judge to be interesting, comprehensive, and non-redundant. In
addition, we find that a text generation model trained on our newly-constructed
dataset is able to offer refinements for novel queries not covered by an
existing taxonomy. Our code and data are available at
this https URL.

该论文提出了实体为中心的查询细化任务，并提出了一种使用现有知识库分类法来产生查询细化数据集的方法，该方法可通过生成模型对新查询进行精细化，并证明了该方法的有效性。

实体中心的查询优化

Entity-Centric Query Refinement

In this paper we analyze the efficiency of various search results
diversification methods. While efficacy of diversification approaches has been
deeply investigated in the past, response time and scalability issues have been
rarely addressed. A unified framework for studying performance and feasibility
of result diversification solutions is thus proposed. First we define a new
methodology for detecting when, and how, query results need to be diversified.
To this purpose, we rely on the concept of "query refinement" to estimate the
probability of a query to be ambiguous. Then, relying on this novel ambiguity
detection method, we deploy and compare on a standard test set, three different
diversification methods: IASelect, xQuAD, and OptSelect. While the first two
are recent state-of-the-art proposals, the latter is an original algorithm
introduced in this paper. We evaluate both the efficiency and the effectiveness
of our approach against its competitors by using the standard TREC Web
diversification track testbed. Results shown that OptSelect is able to run two
orders of magnitude faster than the two other state-of-the-art approaches and
to obtain comparable figures in diversification effectiveness.

通过定义查询细化概率的新方法，并提出了一个统一的框架来研究结果多样化方案的性能和可行性。在标准测试集上，OptSelect 算法相较于 IASelect 和 xQuAD 算法，能够以两个数量级的速度更快地运行，且具有可比效果。