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

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 …

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

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 …

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 …

Biomedical event extraction is critical in understanding biomolecular interactions described
in scientific corpus. One of the main challenges is to identify nested structured events that …

While pre-trained language models (PTLMs) have achieved noticeable success on many
NLP tasks, they still struggle for tasks that require event temporal reasoning, which is …

We present TIMERS-a TIME, Rhetorical and Syntactic-aware model for document-level
temporal relation classification in the English language. Our proposed method leverages …