Abstract Event Causality Extraction (ECE) plays an essential role in many Natural Language
Processing (NLP), such as event prediction and dialogue generation. Recent research in …

Abstract Reasoning about events and their relations attracts surging research efforts since it
is regarded as an indispensable ability to fulfill various event-centric or common-sense …

Current approaches which are mainly based on the extraction of low-level relations among
individual events are limited by the shortage of publicly available labelled data. Therefore …

Identifying causal relationships in a text is essential for achieving comprehensive natural
language understanding. The present work proposes a combination of features derived from …

The detection of textual Emotion-Cause Pair causality is very helpful for improving the
accuracy of emotion-cause extraction and understanding the causes behind specific events …

Abstract Event Causality Identification (ECI) aims at detecting the causal relation between 2
events, which is a challenging task due to the complexity of causal expressions and the …

In this paper, we describe an approach for modelling causal reasoning in natural language
by detecting counterfactuals in text using multi-head self-attention weights. We use pre …

In the digital age, many sources of textual content are devoted to studying and expressing
many sorts of relationships, including employer–employee, if–then, part–whole, product …

Extraction of event causality and especially implicit causality from text data is a challenging
task. Causality is often treated as a specific relation type and can be considered as a part of …

Relations that hold between causes and their effects are fundamental for a wide range of
different sectors. Automatically finding sentences that express such relations may for …