Aliaksandr Huminski
Institute of High Performance Computing, Singapore
(huminskia@ihpc.a-star.edu.sg)
Yan Bin Ng
A*STAR AI, Singapore
(ng_yan_bin@scei.a-star.edu.sg)
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.
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Cite: Huminski, A., & Ng, Y.B. (2020). Automatic extraction of causal chains from text. LIBRES, 29(2), 99-108. https://doi.org/10.32655/LIBRES.2019.2.3