Event-Driven Query Expansion
This work aims to improve search result relevance for users issuing event-related queries, an incremental improvement for web search.
This paper addresses the challenge of improving retrieval performance for event-related web search queries. The authors propose a method that first detects events related to a query and then derives expansion candidates as terms semantically related to both the query and the detected events, showing significant improvement over state-of-the-art methods on newswire TREC datasets.
A significant number of event-related queries are issued in Web search. In this paper, we seek to improve retrieval performance by leveraging events and specifically target the classic task of query expansion. We propose a method to expand an event-related query by first detecting the events related to it. Then, we derive the candidates for expansion as terms semantically related to both the query and the events. To identify the candidates, we utilize a novel mechanism to simultaneously embed words and events in the same vector space. We show that our proposed method of leveraging events improves query expansion performance significantly compared with state-of-the-art methods on various newswire TREC datasets.