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

TL;DR

This paper introduces a novel human-in-the-loop framework for search engines that leverages psycholinguistics and experiment design principles to improve lexical learning efficiency in information retrieval systems.

Contribution

It presents the first end-to-end model combining psycholinguistics and experiment design for optimizing lexical learning in IR through human-machine collaboration.

Findings

Initial simulations demonstrate effective inference process.

Framework shows potential for improved lexical concept learning.

Discussion of preliminary results and future directions.

Abstract

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.