Toba, Hapnes and Wasito, Ito (2010) Reduced Space Classification using Kernel Dimensionality Reduction for Question Classification in Public Health Question-Answering. In: International Conference on Soft Computing, Intelligent System and Information Technology (ICSIIT).
|
Text
10.pdf - Published Version Download (691Kb) | Preview |
Abstract
One of the major problems in Question Answering System is how to classify a question into a particular class that further will be used to find exact answers within a large collection of documents. Kernel Dimensionality Reduction (KDR) is an alternative method that can be used for features reduction, and in the same time classify question type by using the most effective m-dimensional features in its vector space. In this experiment we used question-answer pairs data from public health domain and word (unigram) features construction. This research shows that KDR correct rate performance is better than SVM after a head-to-head comparison from 100 observations.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Uncontrolled Keywords: | Kernel Dimensionality Reduction, Reproducing Kernel Hilbert Space, Supervised Machine Learning, Question Classification, Question Answering System |
| Subjects: | T Technology > T Technology (General) |
| Depositing User: | Perpustakaan Maranatha |
| Date Deposited: | 05 Oct 2018 10:05 |
| Last Modified: | 10 Oct 2018 09:18 |
| URI: | http://repository.maranatha.edu/id/eprint/24874 |
Actions (login required)
 |
View Item |
