Query reformulation

This task is designed to study the impact of domain expertise on query formulation. The crowd workers, divided in two types (expert or novice in the medical domain) were asked to build the appropriate query that allows achieving the search task using a pair of facets: (1) search context and (2) information need. A total of 113 topics (of 2 datasets CLEFeHealth and OHSUMED) were submitted to 3 experts and 3 novices for self-query generation; therefore 6 queries were formulated for each topic. More details of this task can be found in our paper in references. Fields of the csv file: <strong>dataset</strong>: <em>CLEF_eHealth</em> or <em>OHSUMED</em> <strong>topic_id</strong>: ID of the topic (from 1 to 50 for CLEF_eHealth, from 50 to 113 for OHSUMED) <strong>answerer_type</strong>: <em>expert</em> or <em>novice</em> <strong>answerer_id</strong>: ID of crowd worker <strong>formulated</strong><strong>_query</strong>: query formulated by the crowd worker