Event Causality Extraction from Natural Science Literature

Abstract

We aim to develop a text mining framework capable of identifying and extracting causal dependencies among changing variables (or events) from scientific publications in the cross-disciplinary field of oceanographic climate science. The extracted information can be used to infer new knowledge or to find out unknown hypotheses through reasoning, which forms the basis of a knowledge discovery support system. Automatic extraction of causal knowledge from text content is a challenging task. Generally, the approaches of causal relation identification proposed in the literature target specific domain such as online news or biomedicine as the domain has significant influence on causality expressions found in the domain texts. Therefore, the existing models of causality extraction may not be directly portable to other/new domains. In this paper, we describe the nature of causation observed in climate science domain, review the state-of-the-art approaches in causal knowledge extraction from text and carefully select the methods and resources most likely to be applicable to the considered domain.