NarrativeTime: Dense Temporal Annotation on a Timeline
This work addresses the need for dense temporal annotation in natural language processing, enabling more comprehensive analysis of event timelines, though it is incremental as it builds on existing annotation efforts.
The paper tackles the problem of sparse temporal annotation in texts by introducing NarrativeTime, a timeline-based framework that achieves full coverage of all possible temporal links, resulting in a significant increase in annotation density with comparable agreement to previous methods.
For the past decade, temporal annotation has been sparse: only a small portion of event pairs in a text was annotated. We present NarrativeTime, the first timeline-based annotation framework that achieves full coverage of all possible TLinks. To compare with the previous SOTA in dense temporal annotation, we perform full re-annotation of TimeBankDense corpus, which shows comparable agreement with a significant increase in density. We contribute TimeBankNT corpus (with each text fully annotated by two expert annotators), extensive annotation guidelines, open-source tools for annotation and conversion to TimeML format, baseline results, as well as quantitative and qualitative analysis of inter-annotator agreement.