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 …

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

Large Language Models (LLMs) have recently emerged as an effective tool to assist
individuals in writing various types of content, including professional documents such as …

The goal of temporal relation extraction is to infer the temporal relation between two events
in the document. Supervised models are dominant in this task. In this work, we investigate …

Reasoning about time is of fundamental importance. Many facts are time-dependent. For
example, athletes change teams from time to time, and different government officials are …

Identifying temporal and subevent relationships between different events (ie, event relation
extraction) is an important step towards event-centric natural language processing, which …

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 …

Understanding natural language involves recognizing how multiple event mentions
structurally and temporally interact with each other. In this process, one can induce event …

Modelling and understanding time remains a challenge in contemporary video
understanding models. With language emerging as a key driver towards powerful …

This paper focuses on extracting temporal and parent–child relationships between news
events in social news. Previous methods have proved that syntactic features are valid …