Events are occurrences in specific locations, time, and semantics that nontrivially impact
either our society or the nature, such as earthquakes, civil unrest, system failures …

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 …

In software-defined networking (SDN), several controllers improve the reliability as well as
the scalability of networks such as the Internet-of-Things (IoT), with the distributed control …

Identifying causal relations of events is an important task in natural language processing
area. However, the task is very challenging, because event causality is usually expressed in …

Causality extraction from natural language texts is a challenging open problem in artificial
intelligence. Existing methods utilize patterns, constraints, and machine learning techniques …

Event causality identification (ECI) aims to identify causal relations of event mention pairs in
text. Despite achieving certain accomplishments, existing methods are still not effective due …

By studying the causality contained in financial texts, we can further reveal more potential
laws of economic activities, such as “factors promoting stable and healthy economic …

This paper studies event causality identification, which aims at predicting the causality
relation for a pair of events in a sentence. Regarding event causality identification as a …

We present a conditional text generation framework that posits sentential expressions of
possible causes and effects. This framework depends on two novel resources we develop in …

Extracting various types of event relations in financial texts can benefit many downstream
applications supporting financial analysis. This paper addresses the multi-type event …