Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Probabilistic question answering on the web
Proceedings of the 11th international conference on World Wide Web
Question answering from the web using knowledge annotation and knowledge mining techniques
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Evaluating discourse-based answer extraction for why-question answering
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
ACM Transactions on Asian Language Information Processing (TALIP)
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
Using syntactic information for improving why-question answering
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A two-stage approach to retrieving answers for how-to questions
EACL '06 Proceedings of the Eleventh Conference of the European Chapter of the Association for Computational Linguistics: Student Research Workshop
Why text segment classification based on part of speech feature selection
DS'10 Proceedings of the 13th international conference on Discovery science
What is not in the bag of words for why-qa?
Computational Linguistics
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Increased attention has been focused on question answering (QA) technology as next generation search since it improves the usability of information acquisition from web. However, not much research has been conducted on "non-factoid-QA", especially on Why Question Answering (Why-QA). In this paper, we introduce a machine learning approach to automatically construct a classifier with function words as features to perform Why Text Segments Classification (WTS classification) by using SVM. It is a process of detecting text segments describing "reasons-causes" and is a subtask of Why-QA mainly related to an answer extraction part. We argue that function words are a strong discriminator for WTS classification. Furthermore, since function words appear in almost all text segments regardless of the domain of the topic, it also enables construction of a domain independent classifier. The experimental results showed significant improvement over state-of-the-art results in terms of accuracy of WTS classification as well as domain independent capability.