Causality represents the most important kind of correlation between events. Extracting
causali-ty from text has become a promising hot topic in NLP. However, there is no mature …

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 …

Causationin written natural language can express a strong relationship between events and
facts. Causation in the written form can be referred to as a causal relation where a cause …

Causality is an important type of relation which is crucial in numerous tasks, such as
predicting future events, generating scenario, question answering, textual entailment and …

Previous studies on causality estimation mainly aquire causal event pairs from a large
corpus based on lexico-syntactic patterns and coreference relations, and estimate causality …

This work aims to extract causal relations that exist between two events expressed by noun
phrases or sentences. The previous works for the causality made use of causal patterns …

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 …

Various supervised algorithms for mining causal relations from large corpora exist. These
algorithms have focused on relations explicitly expressed with causal verbs, eg “to cause” …

Causality understanding between events is a critical natural language processing task that
is helpful in many areas, including health care, business risk management and finance. On …

The era of big textual corpora and machine learning technologies have paved the way for
researchers in numerous data mining fields. Among them, causality mining (CM) from textual …