Equip** 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 …

Temporal information retrieval has been a topic of great interest in recent years. Its purpose
is to improve the effectiveness of information retrieval methods by exploiting temporal …

Standard test sets for supervised learning evaluate in-distribution generalization.
Unfortunately, when a dataset has systematic gaps (eg, annotation artifacts), these …

Abstract Knowledge Graph (KG) embedding has emerged as an active area of research
resulting in the development of several KG embedding methods. Relational facts in KG often …

Over the past decade, the field of natural language processing has developed a wide array
of computational methods for reasoning about narrative, including summarization …

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

Clinical TempEval 2016 evaluated temporal information extraction systems on the clinical
domain. Nine sub-tasks were included, covering problems in time expression identification …

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 …

Abstract Comparing between Deep Neural Network (DNN) models based on their
performance on unseen data is crucial for the progress of the NLP field. However, these …

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 …