Mining explanation knowledge from textual data

Authors:
Chaveevan Pechsiri;Asanee Kawtrakul;Rapepun Piriyakul
Affiliations:
Department of Computer Engineering, Kasetsart University, Bangkok, Thailand;Department of Computer Engineering, Kasetsart University, Bangkok, Thailand;Department of Computer Engineering, Kasetsart University, Bangkok, Thailand
Venue:
ACST'06 Proceedings of the 2nd IASTED international conference on Advances in computer science and technology
Year:
2006

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Abstract

Mining Know-Why or explanation knowledge will induce a knowledge of reasoning that is beneficial for our daily use in diagnosis. Then, this framework is for discovering causality existing between causative antecedent and effective consequent discourse units. There are two main problems in the causality extraction; cause-effect identification and cause-effect boundary determination. The cause-effect identification problem can be solved by learning verb pairs and lexico syntactic pattern (NP1 V NP2) from annotated corpus, using the Naïve Bayes classifier. The cause-effect boundary determination problem can be solved by using centering theory and interesting cue phrase or causality link, where the interesting cue phrase would include the discourse markers and verb phrases. Our model of causality extraction shows the precision and recall of 86% and 70% respectively, where our evaluation is based on the expert's results.