Time after Time in Words: Chronology through Language Statistics


Previous research has shown that perceptual relations, social affiliations, and geographical locations can be predicted using distributional semantics. We investigated whether this extends to chronological relations. In several computational studies we demonstrated that the chronological order of days, months, years, and the chronological sequence of historical figures can be predicted using language statistics. In fact, both the leaders of the Soviet Union and the presidents of the United States can be ordered chronologically based on the co-occurrences of their names in language. An experiment also showed that the bigram frequency of US president names predicted the response time of participants in their evaluation of the chronology of these presidents. These findings are explained by the Symbol Interdependency Hypothesis which predicts that as a function of language use, language encodes relations in the world around us. Language users can then use language as a cognitive short-cut for mental representations.

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