Mining Causality from Texts for Question Answering System
Chaveevan Pechsiri, Asanee Kawtrakul · IEICE Transactions on Information and Systems · 2007
SUMMARY This research aims to develop automatic knowledge mining of causality from texts for supporting an automatic question answering system (QA) in answering ’why ’ question, which is among the most crucial forms of questions. The out come of this research will assist people in diagnosing problems, such as in plant diseases, health, industrial and etc. While the previous works have extracted causality knowledge within only one or two adjacent EDUs (Elementary Discourse Units), this research focuses to mine causality knowledge existing within multiple EDUs which takes multiple causes and multiple effects in to consideration, where the adjacency between cause and effect is unnecessary. There are two main problems: how to identify the interesting causality events from documents, and how to identify the boundaries of the causative unit and the effective unit in term of the multiple EDUs. In addition, there are at least