We propose the same hybrid approach combining rule-based and supervised classifiers for the identification of causal relations.
while temporal order has a clear formalization in the NLP community, capturing causal relationships in natural language text is more challenging, for they can be expressed by different syntactic and semantic features and involve both situation-specific information and world knowledge.