2016 Fiscal Year Final Research Report
Learning under covariate shift for domain adaptation for word sense disambiguation through weight setting using a outlier detection method
Project/Area Number |
26330244
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Research Category |
Grant-in-Aid for Scientific Research (C)
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Allocation Type | Multi-year Fund |
Section | 一般 |
Research Field |
Intelligent informatics
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Research Institution | Ibaraki University |
Principal Investigator |
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Project Period (FY) |
2014-04-01 – 2017-03-31
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Keywords | 語義曖昧性解消 / 領域適応 / 共変量シフト / 外れ値検出 |
Outline of Final Research Achievements |
In this research, I solved the domain adaptation for word sense disambiguation by using the learning method under the assumption of covariate shift. The key point of this approach is how to estimation of the probability density ratio, and how to conduct the weighted learning. For the first problem, I adopt unconstrained least squares importance fitting (uLSIF). In this research, I showed that a linear kernel is better than a Gaussian kernel used as the basis function generally. Furthermore, I proposed to use 3 kinds of discrete values as a weight. For the second problem, I showed that SVM also is available but the maximum entropy method. Furthermore, I combined the kernel function, the weighted learning and discrete weights.
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Free Research Field |
自然言語処理
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