TimeML-compliant text analysis for temporal reasoning

Reasoning with time 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 TimeML-compliant 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.