AMBRA: A Ranking Approach to Temporal Text Classification

Abstract

This paper describes the AMBRA system, entered in the SemEval-2015 Task 7: 'Diachronic Text Evaluation' subtasks one and two, which consist of predicting the date when a text was originally written. The task is valuable for applications in digital humanities, information systems, and historical linguistics. The novelty of this shared task consists of incorporating label uncertainty by assigning an interval within which the document was written, rather than assigning a clear time marker to each training document. To deal with non-linear effects and variable degrees of uncertainty, we reduce the problem to pairwise comparisons of the form is Document A older than Document B?, and propose a nonparametric way to transform the ordinal output into time intervals.