Using Open Data to Automatically Generate Localized Analogies

Numerical analogies (or “perspectives”) that translate unfamiliar measurements into comparisons with familiar reference objects (e.g., “275,000 square miles is roughly as large as Texas”) have been shown to aid readers’ recall, estimation, and error detection for numbers. However, because familiar reference objects are culture-specific, analogies do not always generalize across audiences. Crowdsourcing perspectives has proven effective but is limited by scalability issues and a lack of crowdworking markets in many regions. In this research, we develop an automated technique for generating localized perspectives. We utilize several open data sources for relevance signals and develop a surprisingly simple model capable of localizing analogies to new audiences without any retraining from human judges. We validate the model by testing it in both a new domain and with a different linguistic audience residing in another country. We release the compiled dataset of 400,000 reference objects to the research community.