TimeML-compliant text analysis for temporal reasoning

Abstract

Reasoning with time 1 needs more than just a list of temporal expressions. TimeML—an emerging standard for temporal annotation as a language capturing properties and relationships among timedenoting expressions and events in text—is a good starting point for bridging the gap between temporal analysis of documents and reasoning with the information derived from them. Hard as TimeMLcompliant analysis is, the small size of the only currently available annotated corpus makes it even harder. We address this problem with a hybrid TimeML annotator, which uses cascaded finite-state grammars (for temporal expression analysis, shallow syntactic parsing, and feature generation) together with a machine learning component capable of effectively using large amounts of unannotated data. 1 Temporal Analysis of Documents Many information extraction tasks limit analysis of time to identifying a narrow class of time expressions, which literally specify a temporal point or an interval. For instance, a recent (2004) ACE task is that of temporal expression recognition and normalisation (TERN; see