Numerous important events happen everyday and everywhere but are reported in different
media sources with different narrative styles. How to detect whether real-world events have …

Event detection (ED), which means identifying event trigger words and classifying event
types, is the first and most fundamental step for extracting event knowledge from plain text …

We consider the task of named entity recognition for Chinese social media. The long line of
work in Chinese NER has focused on formal domains, and NER for social media has been …

We present the first human-annotated dialogue-based relation extraction (RE) dataset
DialogRE, aiming to support the prediction of relation (s) between two arguments that …

Understanding human's language requires complex world knowledge. However, existing
large-scale knowledge graphs mainly focus on knowledge about entities while ignoring …

We describe the evolution of the Entities, Relations and Events (ERE) annotation task,
created to support research and technology development within the DARPA DEFT program …

Event extraction (EE) has considerably benefited from pre-trained language models (PLMs)
by fine-tuning. However, existing pre-training methods have not involved modeling event …

In this work we present a new dataset of literary events—events that are depicted as taking
place within the imagined space of a novel. While previous work has focused on event …

Motivation: The scientific literature embeds an enormous amount of relational knowledge,
encompassing interactions between biomedical entities, like proteins, drugs, and symptoms …

We present SENTiVENT, a corpus of fine-grained company-specific events in English
economic news articles. The domain of event processing is highly productive and various …