Extracting Causal Relations from Emergency Cases Based on Conditional Random Fields

As causality extraction from cases is essential for emergency causal learning, it serves as a foundation for follow-up emergency management. However, there remain barriers to break for applying the previous causality extraction methods to emergency management. The experience of emergency management inspires us that the cause of disasters should have existed in the time before the effect. Therefore, causality relations can be seen as distinct temporal relations. By utilizing the temporal characteristics of causality, this paper redefines the causality extraction as a special kind of temporality extraction and presents a method for extracting causality from emergency cases based on conditional random fields (CRFs). Then the task turns to be a sequence labeling process which can be solved by involving a CRFs model. Several typhoon-related emergency cases are chosen as the experimental dataset. To seek the impact of different features on the model performance, two feature templates are also chosen to train the model. The experimental results show that our approaches can not only deal with marked causal relations, but also work effectively on unmarked causal relations. Besides, the CRFs model can even extract causal relations between sentences.