We examine the capabilities of a unified, multi-task framework for three information
extraction tasks: named entity recognition, relation extraction, and event extraction. Our …

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 …

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 …

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

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

A survey of computational methods for understanding, generating, and manipulating human
language, which offers a synthesis of classical representations and algorithms with …

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 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 …

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