The identification of causal relationships between events or entities within biomedical texts
is of great importance for creating scientific knowledge bases and is also a fundamental …

Background Temporal relations between clinical events play an important role in clinical
assessment and decision making. Extracting such relations from free text data is a …

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 …

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

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

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 …

Classic methods for clinical temporal relation extraction focus on relational candidates within
a sentence. On the other hand, break-through Bidirectional Encoder Representations from …

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 …

We propose TRACIE, a novel temporal reasoning dataset that evaluates the degree to which
systems understand implicit events--events that are not mentioned explicitly in natural …