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In this paper we study the identification and verification of simple claims about statistical properties, e.g. claims about the population or the inflation rate of a country. We show that this problem is similar to extracting numerical information from text and following recent work, instead of annotating data for each property of interest in order to learn supervised models, we develop a distantly supervised baseline approach using a knowledge base and raw text. In experiments on 16doi:10.18653/v1/d15-1312 dblp:conf/emnlp/VlachosR15 fatcat:nbljdckknzarvlcs7vbg3oss5a