Know-Why Extraction from Textual Data for Supporting What Questions

This research aims to automatically extract Know-Why from documents on the website to contribute knowledge sources to support the question-answering system, especially What-Question, for disease treatment. This paper is concerned about extracting Know-Why based on multiple EDUs (Elementary Discourse Units). There are two problems in extracting Know-Why: an identification problem and an effect boundary determination problem. We propose using Naive Bayes with three verb features, a causative-verb-phrase concept set, a supporting causative verb set, and the effect-verb-phrase concept set. The Know-Why extraction results show the success rate of 85.5% precision and 79.8% recall.

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