Annotating Temporal Relations in Different Types of Text

Yingying Chen

Date: 2022-02-04 / 4:00 ~ 5:00 PM
Location: MSC E306 (


Detecting the temporal relations of events in a text is a complicated natural language understanding task. However, figuring out the timeline of events is key to improving machine comprehension. Previous work specified approaches to identifying events in texts, proposing appropriate temporal relations and ways to order events with respect to one another. However, the vast majority of existing temporal dependency annotation has been carried out on simple narrative text or news sources. The annotation schemes are not always applicable to noisy, highly variable, social media texts such as Reddit posts. We devise a more generalized and robust scheme to support a broader range of text annotation. In this research, we aim to 1) improve existing annotation guidelines for more complex sentence structures, 2) evaluate the annotation performance among student annotators to achieve competitive inter-annotator agreement scores, 3) quantify the characteristics unique to Reddit text and provide a statistical analysis of the difficulties encountered when annotating Reddit data, and 4) compare and contrast the effectiveness of our temporal annotation scheme across three diverse sources: children’s stories, social media texts, and news articles.