  In search engines, query expansion (QE) is a crucial technique to improve
search experience. Previous studies often rely on long-term search log mining,
which leads to slow updates and is sub-optimal for time-sensitive news
searches. In this work, we present Event-Centric Query Expansion (EQE), a novel
QE system that addresses these issues by mining the best expansion from a
significant amount of potential events rapidly and accurately. This system
consists of four stages, i.e., event collection, event reformulation, semantic
retrieval and online ranking. Specifically, we first collect and filter news
headlines from websites. Then we propose a generation model that incorporates
contrastive learning and prompt-tuning techniques to reformulate these
headlines to concise candidates. Additionally, we fine-tune a dual-tower
semantic model to function as an encoder for event retrieval and explore a
two-stage contrastive training approach to enhance the accuracy of event
retrieval. Finally, we rank the retrieved events and select the optimal one as
QE, which is then used to improve the retrieval of event-related documents.
Through offline analysis and online A/B testing, we observe that the EQE system
significantly improves many metrics compared to the baseline. The system has
been deployed in Tencent QQ Browser Search and served hundreds of millions of
users. The dataset and baseline codes are available at
https://open-event-hub.github.io/eqe .
