Large, pre-trained language models (PLMs) such as BERT and GPT have drastically
changed the Natural Language Processing (NLP) field. For numerous NLP tasks …

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 …

Most existing joint neural models for Information Extraction (IE) use local task-specific
classifiers to predict labels for individual instances (eg, trigger, relation) regardless of their …

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 …

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 …

This paper focuses on how to take advantage of external knowledge bases (KBs) to improve
recurrent neural networks for machine reading. Traditional methods that exploit knowledge …

Events and entities are closely related; entities are often actors or participants in events and
events without entities are uncommon. The interpretation of events and entities is highly …

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