Semantic representation is receiving growing attention in NLP in the past few years, and
many proposals for semantic schemes (eg, AMR, UCCA, GMB, UDS) have been put forth …

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 …

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 …

Besides entity-centric knowledge, usually organized as Knowledge Graph (KG), events are
also an essential kind of knowledge in the world, which trigger the spring up of event-centric …

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

Existing temporal relation (TempRel) annotation schemes often have low inter-annotator
agreements (IAA) even between experts, suggesting that the current annotation task needs …

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 …

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 …

A critical part of reading is being able to understand the temporal relationships between
events described in a passage of text, even when those relationships are not explicitly …

Temporal common sense (eg, duration and frequency of events) is crucial for understanding
natural language. However, its acquisition is challenging, partly because such information is …