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 …

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, 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 …

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 …

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 extraction plays an important role in natural language processing (NLP) applications
including question answering and information retrieval. Traditional event extraction relies …

We study the event detection problem using convolutional neural networks (CNNs) that
overcome the two fundamental limitations of the traditional feature-based approaches to this …