Classifying Term Variants in Query Formulation

Formulating queries is a challenging stage of the search process. This study investigates how crowd workers formulate an initial query for a common information need described in a backstory, resulting in diverse query variations. Using the UQV100 dataset of information need backstories and corresponding queries, we analyze the variations. Our findings show that 70% of the query terms used in crowd worker queries did not appear in the backstory text. Examining such terms we developed a taxonomy of search strategies, with the most common being semantic variations of backstory terms, followed by information type specifications. Additionally, we categorized the backstories by cognitive complexity, showing that higher complexity led to greater diversity in query variations and a wider range of term variant categories. This study highlights the importance of accounting for query variations, term variants, user strategies, and cognitive complexity in designing search systems and test collections to better align with users' information needs, influenced by the cognitive demands of a task, and enhance system performance and usability.