  Information retrieval (IR) systems need to constantly update their knowledge
as target objects and user queries change over time. Due to the power-law
nature of linguistic data, learning lexical concepts is a problem resisting
standard machine learning approaches: while manual intervention is always
possible, a more general and automated solution is desirable. In this work, we
propose a novel end-to-end framework that models the interaction between a
search engine and users as a virtuous human-in-the-loop inference. The proposed
framework is the first to our knowledge combining ideas from psycholinguistics
and experiment design to maximize efficiency in IR. We provide a brief overview
of the main components and initial simulations in a toy world, showing how
inference works end-to-end and discussing preliminary results and next steps.
