A Hybrid Approach for the Automatic Extraction of Causal Relations from Text

This chapter presents an approach for the discovery of causal relations from open domain text in English. The approach is hybrid, indeed it joins rules based and machine learning methodologies in order to combine the advantages of both. The approach first identifies a set of plausible cause-effect pairs through a set of logical rules based on dependencies between words, then it uses Bayesian inference to reduce the number of pairs produced by ambiguous patterns. The SemEval-2010 task 8 dataset challenge has been used to evaluate our model. The results demonstrate the ability of the rules for the relation extraction and the improvements made by the filtering process.