Exploring task-based query expansion at the TREC-COVID track
This work addresses search query effectiveness for researchers in the COVID-19 domain, but it is incremental as it builds on existing methods with modest gains.
The paper tackled the problem of generating effective queries for search tasks at the TREC-COVID track by exploring task-based query expansion, resulting in a slight improvement in NDCG@20 scores over a BM25 baseline.
We explore how to generate effective queries based on search tasks. Our approach has three main steps: 1) identify search tasks based on research goals, 2) manually classify search queries according to those tasks, and 3) compare three methods to improve search rankings based on the task context. The most promising approach is based on expanding the user's query terms using task terms, which slightly improved the NDCG@20 scores over a BM25 baseline. Further improvements might be gained if we can identify more specific search tasks.