Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/154414
Title: Automatic extraction of causal chains from text
Authors: Huminski, Aliaksandr
Ng, Yan Bin
Keywords: Library and information science
Issue Date: 2020
Source: Huminski, A. & Ng, Y. B. (2020). Automatic extraction of causal chains from text. Library and Information Science Research E-Journal, 29(2), 99-108. https://dx.doi.org/10.32655/LIBRES.2019.2.3
Journal: Library and Information Science Research E-Journal 
Abstract: 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.
URI: https://hdl.handle.net/10356/154414
ISSN: 1058-6768
DOI: 10.32655/LIBRES.2019.2.3
Rights: © 2020 Aliaksandr Huminski, Ng Yan Bin. All rights reserved.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:Library and Information Science Research E-journal (LIBRES)

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