Equipping machines with comprehensive knowledge of the world's entities and their
relationships has been a longstanding goal of AI. Over the last decade, large-scale …

This paper presents an exhaustive quantitative and qualitative evaluation of Large
Language Models (LLMs) for Knowledge Graph (KG) construction and reasoning. We …

The unprecedented performance of large language models (LLMs) necessitates
improvements in evaluations. Rather than merely exploring the breadth of LLM abilities, we …

Large Language Models (LLMs) have made remarkable strides in various tasks. Whether
LLMs are competitive few-shot solvers for information extraction (IE) tasks, however, remains …

Recently, considerable literature has grown up around the theme of few-shot named entity
recognition (NER), but little published benchmark data specifically focused on the practical …

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

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 …

Social events provide valuable insights into group social behaviors and public concerns and
therefore have many applications in fields such as product recommendation and crisis …

Recognizing facts is the most fundamental step in making judgments, hence detecting
events in the legal documents is important to legal case analysis tasks. However, existing …

The diverse relationships among real-world events, including coreference, temporal, causal,
and subevent relations, are fundamental to understanding natural languages. However, two …