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 …

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

Tweets are the most up-to-date and inclusive stream of in-formation and commentary on
current events, but they are also fragmented and noisy, motivating the need for systems that …

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 …

Hand-coded scripts were used in the 1970-80s as knowledge backbones that enabled
inference and other NLP tasks requiring deep semantic knowledge. We propose …

We propose a joint event and temporal relation extraction model with shared representation
learning and structured prediction. The proposed method has two advantages over existing …

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 …

Understanding events entails recognizing the structural and temporal orders between event
mentions to build event structures/graphs for input documents. To achieve this goal, our …

Understanding temporal and causal relations between events is a fundamental natural
language understanding task. Because a cause must be before its effect in time, temporal …