Traditional approaches to the task of ACE event extraction usually depend on manually
annotated data, which is often laborious to create and limited in size. Therefore, in addition …

Event extraction is of practical utility in natural language processing. In the real world, it is a
common phenomenon that multiple events existing in the same sentence, where extracting …

The current neural network models for event detection have only considered the sequential
representation of sentences. Syntactic representations have not been explored in this area …

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 remains a challenge because of the difficulty of encoding the word
semantics in various contexts. Previous approaches have heavily depended on language …

Event extraction (EE) is a crucial research task for promptly apprehending event information
from massive textual data. With the rapid development of deep learning, EE based on deep …

This paper tackles the task of event detection (ED), which involves identifying and
categorizing events. We argue that arguments provide significant clues to this task, but they …

Few-shot Learning (FSL) is aimed to make predictions based on a limited number of
samples. Structured data such as knowledge graphs and ontology libraries has been …

Most previous event extraction studies have relied heavily on features derived from
annotated event mentions, thus cannot be applied to new event types without annotation …

Event detection (ED), a key subtask of information extraction, aims to recognize instances of
specific event types in text. Previous studies on the task have verified the effectiveness of …