Automatic extraction of causal chains from text
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 cau...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2021
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/154414 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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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