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 …

Neural network based models commonly regard event detection as a word-wise
classification task, which suffer from the mismatch problem between words and event …

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 …

Few-shot named entity recognition (NER) exploits limited annotated instances to identify
named mentions. Effectively transferring the internal or external resources thus becomes the …

For the Chinese language, event triggers in a sentence may appear inside or across words
after word segmentation. Thus recent works on Chinese event detection often formulate the …

As a paratactic language, sentence-level argument extraction in Chinese suffers much from
the frequent occurrence of ellipsis with regard to inter-sentence arguments. To resolve such …

Event detection (ED) seeks to recognize event triggers and classify them into the predefined
event types. Chinese ED is formulated as a character-level task owing to the uncertain word …

Event extraction generally suffers from the data sparseness problem. In this paper, we
address this problem by utilizing the labeled data from two different languages. As a …

Event extraction is an essential but challenging task in information extraction. This task has
considerably benefited from pre-trained language models, such as BERT. However, when it …

Argument extraction is a challenging task in event extraction. However, most of previous
studies focused on intra-sentence information and failed to extract inter-sentence …