Machine Learning
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
An unsupervised approach to recognizing discourse relations
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Building a discourse-tagged corpus in the framework of Rhetorical Structure Theory
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Mining Causality from Texts for Question Answering System
IEICE - Transactions on Information and Systems
Causal relation extraction using cue phrase and lexical pair probabilities
IJCNLP'04 Proceedings of the First international joint conference on Natural Language Processing
Addressing how-to questions using a spoken dialogue system: a viable approach?
KRAQ '09 Proceedings of the 2009 Workshop on Knowledge and Reasoning for Answering Questions
Automatic identification of cause-effect relations in tamil using CRFs
CICLing'11 Proceedings of the 12th international conference on Computational linguistics and intelligent text processing - Volume Part I
Knowledge and reasoning for question answering: Research perspectives
Information Processing and Management: an International Journal
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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 Naïve 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.