Entity oriented task extraction from query logs

Identifying user tasks from query logs has garnered considerable interest from the research community lately. Several approaches have been proposed to extract tasks from search sessions. Current approaches segment a user session into disjoint tasks using features extracted from query, session or clicked document text. However, user tasks most often than not are entity centric and text based features will not exploit entities directly for task extraction. In this work, we explore entity specific task extraction from search logs. We evaluate the quality of extracted tasks with Session track data. Empirical evaluation shows that terms associated with entity oriented tasks can not only be used to predict terms in user sessions but also improve retrieval when used for query expansion.