Name
Title | ||
|---|---|---|
Using KCCA for Japanese–English cross-language information retrieval and document classification |
Abstract | ||
|---|---|---|
Kernel Canonical Correlation Analysis (KCCA) is a method of correlating linear relationship between two variables in a kernel defined feature space. A machine learning algorithm based on KCCA is studied for cross-language information retrieval. We apply the algorithm in Japanese–English cross-language information retrieval. The results are quite encouraging and are significantly better than those obtained by other state of the art methods. Computational complexity is an important issue when applying KCCA to large dataset as in information retrieval. We experimentally evaluate several methods to alleviate the problem of applying KCCA to large datasets. We also investigate cross-language document classification using KCCA as well as other methods. Our results show that it is feasible to use a classifier learned in one language to classify the documents in other languages. |
Year | DOI | Venue |
|---|---|---|
2006 | https://doi.org/10.1007/s10844-006-1627-y | Journal of Intelligent Information Systems |
Keywords | Field | DocType |
Cross-language information retrieval,Machine learning,Kernel canonical correlation analysis,Unsupervised learning,Cross-language Japanese–English document retrieval and classification | Kernel (linear algebra),Document classification,Data mining,Kernel canonical correlation analysis,Feature vector,Computer science,Unsupervised learning,Artificial intelligence,Classifier (linguistics),Cross-language information retrieval,Machine learning,Computational complexity theory | Journal |
Volume | Issue | ISSN |
27 | 2 | 0925-9902 |
Citations | PageRank | References |
31 | 2.67 | 8 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaoyong Li | 1 | 393 | 26.55 |
| John Shawe-Taylor | 2 | 11879 | 1518.73 |