Identification and Verification of Simple Claims about Statistical Properties

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 coun- try. 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 inter- est in order to learn supervised models, we develop a distantly supervised base- line approach using a knowledge base and raw text. In experiments on 16 statistical properties about countries from Freebase we show that our approach identifies sim- ple statistical claims about properties with 60% precision, while it is able to verify these claims without requiring any explicit supervision for either tasks. Furthermore, we evaluate our approach as a statistical property extractor and we show it achieves 0.11 mean absolute percentage error.