Mining Causality Knowledge From Thai Textual Data

Mining causality knowledge will induce knowledge of reasoning beneficial for our daily use in diagnosis. This framework is for discovering causality existing between causative antecedent and effective consequent discourse units. There are two main problems in causality extraction; causeeffect identification and cause-effect boundary determination. The causeeffect identification problem can be solved by learning verb pairs among different elementary discourse units and learning lexico syntactic pattern (i.e., NP1 V NP2) within a single elementary discourse unit from annotated corpus, by using the Naïve Bayes classifier. WordNet will be used in this learning for providing the concept for the verb pairs and NP pair of the lexico syntactic pattern after translation of Thai words to English words by using the ThaiEnglish dictionary. The cause-effect boundary determination problem can be solved by using centering theory and cue phrase or causality link, where the cue phrase would include the discourse markers and verb phrases. Our model of causality extraction shows precision and recall, of 86% and 70% respectively. Our evaluation is based on the expert’s results.