Deconstructing complex search tasks: a bayesian nonparametric approach for extracting sub-tasks

Search tasks, comprising a series of search queries serving a common informational need, have steadily emerged as accurate units for developing the next generation of task-aware web search systems. Most prior research in this area has focused on segmenting chronologically ordered search queries into higher level tasks. A more naturalistic viewpoint would involve treating query logs as convoluted structures of tasks-subtasks, with complex search tasks being decomposed into more focused sub-tasks. In this work, we focus on extracting sub-tasks from a given collection of on-task search queries. We jointly leverage insights from Bayesian nonparametrics and word embeddings to identify and extract sub-tasks from a given collection of ontask queries. Our proposed model can inform the design of the next generation of task-based search systems that leverage user’s task behavior for better support and personalization.