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

As a core task and an important link in the fields of natural language understanding and
information retrieval, information extraction (IE) can structure and semanticize unstructured …

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 …

In this paper, we propose an effective yet efficient model PAIE for both sentence-level and
document-level Event Argument Extraction (EAE), which also generalizes well when there is …

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

Knowledge Graphs (KGs) play a pivotal role in advancing various AI applications, with the
semantic web community's exploration into multi-modal dimensions unlocking new avenues …

We consider event extraction in a generative manner with template-based conditional
generation. Although there is a rising trend of casting the task of event extraction as a …

Generative Knowledge Graph Construction (KGC) refers to those methods that leverage the
sequence-to-sequence framework for building knowledge graphs, which is flexible and can …

Event extraction is an important task in natural language processing that focuses on mining
event-related information from unstructured text. Despite considerable advancements, it is …

Large Language Model (LLM) trained on a mixture of text and code has demonstrated
impressive capability in translating natural language (NL) into structured code. We observe …