Richer Event Description: Integrating event coreference with temporal, causal and bridging annotation

By Timothy J. O'Gorman, Kristin Wright-Bettner, Martha Palmer. 2016

168 citations recorded by the source corpus.

There have been a wide range of recent annotated corpora concerning events, either regarding event coreference, the temporal order of events, hierarchical “subevent” structure of events, or causal relationships between events. However, although some believe that these different phenomena will display rich interactions, relatively few corpora annotate all of those layers of annotation in a unified fashion. This paper describes the annotation methodology for the Richer Event Descriptions corpus, which annotates entities, events, times, their coreference and partial coreference relations, and the temporal, causal and subevent relationships between the events. It suggests that such rich annotations of within-document event phenomena can be built with high quality through a multi-stage annotation pipeline, and that the resultant corpus could be useful for systems hoping to transition from the detection of isolated mentions of events toward a richer understanding of events grounded in the temporal, causal, referential and bridging relations that define them.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Xiang Ren
2. Nanyun Peng
3. Ashish Sabharwal
4. Dan Roth
5. Tushar Khot
6. Kyle Richardson
7. Mohit Bansal
8. Rujun Han

Citations in the corpus

• ECONET: Effective Continual Pretraining of Language Models for Event Temporal Reasoning (2020, methodology): “We evaluate our approach on five datasets concerning temporal ERE and MRC/QA.”
• Domain Knowledge Empowered Structured Neural Net for End-to-End Event Temporal Relation Extraction (2020, unknown): “, 2003) and RED (O’Gorman et al., 2016) consist of expert annotations of news articles.”
• Probing Prior Knowledge Needed in Challenging Chinese Machine Reading Comprehension (2019, background): “• Precondition† : If had event A not happened, event B would not have happened (Ikuta et al., 2014; O’Gorman et al., 2016).”
• Deep Structured Neural Network for Event Temporal Relation Extraction (2019, background): “, 2003) and RED (O’Gorman et al., 2016) facilitate the research in temporal relation extraction.”
• Contextualized Word Embeddings Enhanced Event Temporal Relation Extraction for Story Understanding (2019, background): “RED (O’Gorman et al., 2016) contains annotations of rich relations be-tween event pairs for storyline understanding, including co-reference and partial co-reference relations, temporal; causal, and sub-event relations.”
• Joint Event and Temporal Relation Extraction with Shared Representations and Structured Prediction (2019, background): “, 2003) and RED (O’Gorman et al., 2016) facilitate the research in temporal relation extraction.”
• Temporal Reasoning on Implicit Events from Distant Supervision (2020, background): “Temporal reasoning has received much attention in the NLP community, and to date, there are many datasets that focus on temporal ordering (Pustejovsky et al., 2003; Bethard et al., 2007; Cassidy et al., 2014; Reimers et al., 2016; O’Gorman et al., 2016; Ning et al., 2018b, 2020b), and other temporal”
• Resource Interoperability for Sustainable Benchmarking: The Case of Events (2018, background): “(O’Gorman et al., 2016) a number of event-related annotation layers are integrated into a single representation of events and participants.”
• Joint Constrained Learning for Event-Event Relation Extraction (2020, methodology): “In addition to these two datasets, we also present a case study on an updated version of the RED dataset (O’Gorman et al., 2016).”
• A Multi-Axis Annotation Scheme for Event Temporal Relations (2018, methodology): “…2007, 2010; UzZa-man et al., 2013), TimeBank-Dense (TB-Dense) (Cassidy et al., 2014), EventTimeCorpus (Reimers et al., 2016), and datasets with both temporal and other types of relations (e.g., coreference and causality) such as CaTeRs (Mostafazadeh et al., 2016) and RED (O’Gorman et al., 2016).”