Automatic extraction of causal chains from text
Aliaksandr Huminski, Yan Bin Ng · Library and Information Science Research E-Journal · 2020
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.