TimeML-Compliant Analysis of Text Documents

Reasoning with temporal information1 requires a representation of time considerably more involved than just a list of temporal expressions—which typically define the extent of current time extraction efforts. TimeML is an emerging standard for temporal annotation, defining a language for expressing properties and relationships among timedenoting expressions and events in free text. This paper takes the position that TimeML is a good starting point for bridging the gap between temporal analysis of documents and reasoning with information derived from these documents. TimeML-compliant analysis is hard; and the task is made even harder by the small size of the only annotated corpus available to date. To address this, and related, challenges, we have developed and implemented a hybrid TimeML annotator, which uses cascaded finite-state grammars (for temporal expresison analysis, shallow syntactic parsing, and feature generation) together with a machine learning component capable of effectively using large amounts of unannotated data. We motivate our mixed strategy; this is work in progress, and we report interim results on the first effort to use the TIMEBANK corpus for building an operational TimeML analyser. This work was supported by the Advanced Research and Development Activity under the Novel Intelligence and Massive Data (NIMD) program PNWD-SW-6059.