Name
Abstract | ||
|---|---|---|
Question Classification is an important stage in Question Answering, and it has been a hot topic in the field of Information Retrieval in recent years. In this paper we explore the role of semantic features and propose two separate tree kernel functions incorporating the semantic features into the Support Vector Machine model. Then Multiple Kernel Learning approach is proposed to combine the two kernels and gather their advantages together. Experimental results show that using the method proposed in this paper is very effective and the accuracy reaches 95.8% which significantly outperforms the state-of-the-art approaches. © 2011 Academy Publisher. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.4304/jcp.6.11.2325-2334 | JCP |
Keywords | Field | DocType |
multiple kernel learning,question classification,semantic features,support vector machine,tree kernel | Graph kernel,Radial basis function kernel,Pattern recognition,Computer science,Kernel embedding of distributions,Support vector machine,Multiple kernel learning,Tree kernel,Polynomial kernel,Artificial intelligence,Kernel method,Machine learning | Journal |
Volume | Issue | Citations |
6 | 11 | 1 |
PageRank | References | Authors |
0.36 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guohua Chen | 1 | 5 | 2.15 |
| Yong Tang | 2 | 554 | 76.46 |
| Yan Pan | 3 | 179 | 19.23 |
| Qiang Deng | 4 | 20 | 3.04 |