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 .

翻译：在搜索引擎中，查询扩展（QE）是提升搜索体验的关键技术。以往的研究通常依赖长期搜索日志挖掘，导致更新缓慢，且对时间敏感的新闻搜索并非最优。本文提出基于事件的查询扩展（EQE），一种新颖的QE系统，通过快速准确地从大量潜在事件中挖掘最佳扩展来解决上述问题。该系统包含四个阶段：事件收集、事件重构、语义检索和在线排序。具体而言，我们首先从网站收集并筛选新闻标题。随后，我们提出一个融合对比学习和提示微调技术的生成模型，将这些标题重构为简洁的候选内容。此外，我们微调一个双塔语义模型作为事件检索的编码器，并探索两阶段对比训练方法以提升事件检索的准确性。最后，我们对检索到的事件进行排序，选取最优事件作为QE，进而改进与事件相关文档的检索。通过离线分析和在线A/B测试，我们发现EQE系统在多项指标上相比基线有显著提升。该系统已部署于腾讯QQ浏览器搜索，服务于数亿用户。数据集和基线代码详见https://open-event-hub.github.io/eqe。