CRET: A Tool for Automatic Extraction of Causal Relations

There is an interest in extracting knowledge and retrieving information automatically from the current availability of a large collection of electronic resources and from the academic literature available on the Web. In this work a tool called Causal Relation Extraction Tool (CRET) has been developed to extract causal relations from texts. The tool is a Relation Parser to extract relation patterns from medical documents. The causal patterns are detected through a fuzzy matching process between the causal patterns database and partial detected string patterns in the electronic medical documents. The extracted knowledge is stored as an index for the documents and the researchers can consult the indexed databases. The main contribution of this work is a method for cause, effect and condition extraction using a fuzzy relation. The causal extraction method is based on extracted noun and adjectival phrases associated with causal verb (patterns). Quantitative matrices measurements like, Precision, recall, and F-score for the classifiers and the causal pattern extraction were used and computed to evaluate our result. The results indicate that CRET generates 77% of the keywords and the casual relations which have manually been associated by human expert.