Event extraction is challenging due to the complex structure of event records and the
semantic gap between text and event. Traditional methods usually extract event records by …

Event extraction (EE) is a crucial information extraction task that aims to extract event
information in texts. Previous methods for EE typically model it as a classification task, which …

The problem of event extraction requires detecting the event trigger and extracting its
corresponding arguments. Existing work in event argument extraction typically relies heavily …

Event extraction requires high-quality expert human annotations, which are usually
expensive. Therefore, learning a data-efficient event extraction model that can be trained …

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 …

Event extraction, which aims to identify event triggers of pre-defined event types and their
arguments of specific roles, is a challenging task in NLP. Most traditional approaches …

We consider event extraction in a generative manner with template-based conditional
generation. Although there is a rising trend of casting the task of event extraction as a …

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 …

Event extraction is a particularly challenging problem in information extraction. The stateof-
the-art models for this problem have either applied convolutional neural networks in a …

Traditional approaches to the task of ACE event extraction primarily rely on elaborately
designed features and complicated natural language processing (NLP) tools. These …