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Information retrieval (IR) systems need to constantly update their knowledgeas target objects and user queries change over time. Due to the power-lawnature of linguistic data, learning lexical concepts is a problem resistingstandard machine learning approaches: while manual intervention is alwayspossible, a more general and automated solution is desirable. In this work, wepropose a novel end-to-end framework that models the interaction between asearch engine and users as a virtuous human-in-the-loop inference. The proposedframework is the first to our knowledge combining ideas from psycholinguisticsand experiment design to maximize efficiency in IR. We provide a brief overviewof the main components and initial simulations in a toy world, showing howinference works end-to-end and discussing preliminary results and next steps.

Lexical Learning as an Online Optimal Experiment: Building Efficient Search Engines through Human-Machine Collaboration