As an essential component of human cognition, cause–effect relations appear frequently in
text, and curating cause–effect relations from text helps in building causal networks for …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Automatic extraction of cause-effect relationships from natural language texts is a
challenging open problem in Artificial Intelligence. Most of the early attempts at its solution …

Causal relation extraction is a challenging yet very important task for Natural Language
Processing (NLP). There are many existing approaches developed to tackle this task, either …

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 …

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 …

We present CATENA, a sieve-based system to perform temporal and causal relation
extraction and classification from English texts, exploiting the interaction between the …

Commonsense causal reasoning is the process of capturing and understanding the causal
dependencies amongst events and actions. Such events and actions can be expressed in …

Causal reasoning, the ability to identify cause-and-effect relationship, is crucial in human
thinking. Although large language models (LLMs) succeed in many NLP tasks, it is still …

In this work we present an annotation framework to capture causality between events,
inspired by TimeML, and a language resource covering both temporal and causal relations …