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 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 …

This special issue includes eight original works that detail the further developments of ELMs
in theories, applications, and hardware implementation. In" Representational Learning with …

In this paper, the authors consider argument mining as the task of building a formal
representation for an argumentative piece of text. Their goal is to provide a critical survey of …

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 …

The problem we tackle in this work is, given a present news event, to generate a plausible
future event that can be caused by the given event. We present a new methodology for …

In this paper we have proposed a linguistically informed recursive neural network
architecture for automatic extraction of cause-effect relations from text. These relations can …

Despite the importance of understanding causality, corpora addressing causal relations are
limited. There is a discrepancy between existing annotation guidelines of event causality …

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 …