EventFull: Complete and Consistent Event Relation Annotation

MEANTIME (Minard et al., 2016), and EventStoryLine Identifying the semantic relations between events (Caselli and Vossen, 2017) restrict event mentioned in a text, notably temporal, causal and pairs to a span of two consecutive sentences. This coreference relations, has been a fundamental goal limitation inherently prevents testing and training in NLP. Substantial efforts have been devoted to developing models on longer-range relations. Other datasets, various datasets that capture some or all of such as TimeBank (Pustejovsky et al., 2003b) and these relations (O'Gorman et al., 2016; Hong et al., MAVEN-ERE (Wang et al., 2022), did not publish 2016; Wang et al., 2022). These datasets were then a systematic annotation execution protocol that leveraged to develop and to evaluate corresponding guarantees actual complete annotation, and were models for detecting event-event relations (Hu subsequently criticized for being incomplete in et al., 2023; Guan et al., 2024). The output of such their relation annotation (Pustejovsky and Stubbs, models has been utilized in a range of downstream 2011; Rogers et al., 2024). Further, some researchers applications, with recent examples including event aimed to avoid the cost of manual annotation forecasting (Ma et al., 2023), misinformation detection altogether and employed fully-or partlyautomatic (Lei and Huang, 2023), and treatment timeline dataset creation methods (Mirza et al., extraction (Yao et al., 2024), among others.