Information extraction suffers from its varying targets, heterogeneous structures, and
demand-specific schemas. In this paper, we propose a unified text-to-structure generation …

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 …

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 (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 …

Sequential labeling-based NER approaches restrict each word belonging to at most one
entity mention, which will face a serious problem when recognizing nested entity mentions …

Event detection (ED) aims to locate trigger words in raw text and then classify them into
correct event types. In this task, neural net-work based models became mainstream in re …

Event detection has long been troubled by the\emph {trigger curse}: overfitting the trigger will
harm the generalization ability while underfitting it will hurt the detection performance. This …

Chinese pre-trained language models usually process text as a sequence of characters,
while ignoring more coarse granularity, eg, words. In this work, we propose a novel pre …

Event detection systems rely on discrimination knowledge to distinguish ambiguous trigger
words and generalization knowledge to detect unseen/sparse trigger words. Current neural …

More and more, large-scale pre-trained models show apparent advantages in solving the
event detection (ED), ie, a task to solve the problem of event classification by identifying …