Automatic extraction of causal chains from text - Huminski, Aliaksandr (2021)
Background. Automatic extraction of causal chains is valuable for discovering previously unknown and hidden connections between events. However, there is only a handful of works devoted to automatic extraction of causal chains from text. Objective. To develop a method for automatic extraction of causal chains from text. Method. A new approach based on linguistic templates is suggested for causal chain extraction. It is domain-independent, not restricted to extraction from single sentences and unfolded on big data. For implementation, a sequence of four modules was deployed. These are verb restriction, part-of-speech tagging, extracting causal relations, and uniﬁcation and matching events. Results. 14,821 causal chains (with length=2) have been extracted from 100,000 English Wikipedia articles. Contributions. The extracted causal chains can contribute to developing commonsense knowledge bases, reasoning resources, problem-solving, and generally in discovering previously unknown relationships between entities/events.