Event Causality Extraction from Natural Science Literature
| Autor: | Erwin Marsi, Biswanath Barik, Pinar Öztürk |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Causality (physics)
Computer science Event (relativity) 0206 medical engineering 0202 electrical engineering electronic engineering information engineering Natural science 020201 artificial intelligence & image processing 02 engineering and technology General Medicine 020601 biomedical engineering Data science |
| Zdroj: | Research on Computing Science |
| ISSN: | 1870-4069 |
| DOI: | 10.13053/rcs-117-1-8 |
| Popis: | We aim to develop a text mining framework capable ofidentifying and extractingcausal dependenciesamongchanging variables(orevents) from scientific publications in the cross-disciplinary field ofoceanographic climate science. The extracted information can be usedto infer new knowledge or to find out unknown hypotheses throughreasoning, which forms the basis of a knowledge discovery supportsystem. Automatic extraction of causal knowledge from text contentis a challenging task. Generally, the approaches of causal relationidentification proposed in the literature target specific domain such asonline news or biomedicine as the domain has significant influence oncausality expressions found in the domain texts. Therefore, the existingmodels of causality extraction may not be directly portable to other/newdomains. In this paper, we describe the nature of causation observed inclimate science domain, review the state-of-the-art approaches in causalknowledge extraction from text and carefully select the methods andresources most likely to be applicable to the considered domain. Research in Computing Science, ISSN 1870-4069, is an internationally refereed open access scientific research journal published by the National Polytechnic Institute, Mexico. |
| Databáze: | OpenAIRE |
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