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 …

Most existing event extraction (EE) methods merely extract event arguments within the
sentence scope. However, such sentence-level EE methods struggle to handle soaring …

Recent studies on event detection (ED) haveshown that the syntactic dependency graph
canbe employed in graph convolution neural net-works (GCN) to achieve state-of-the-art per …

Recognizing facts is the most fundamental step in making judgments, hence detecting
events in the legal documents is important to legal case analysis tasks. However, existing …

Event Detection (ED) aims to recognize mentions of events (ie, event triggers) and their
types in text. Recently, several ED datasets in various domains have been proposed …

Event Detection (ED) is a fundamental task in automatically structuring texts. Due to the
small scale of training data, previous methods perform poorly on unseen/sparsely labeled …

Event is a common but non-negligible knowledge type. How to identify events from texts,
extract their arguments, even analyze the relations between different events are important …

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 …

Existing event extraction methods classify each argument role independently, ignoring the
conceptual correlations between different argument roles. In this paper, we propose a …