Cross-Domain Academic Search using Structural Correspondence Learning
Project/Area Number |
24700137
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Research Category |
Grant-in-Aid for Young Scientists (B)
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Allocation Type | Multi-year Fund |
Research Field |
Intelligent informatics
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Research Institution | The University of Tokyo |
Principal Investigator |
MORI Junichiro 東京大学, 工学(系)研究科(研究院), 講師 (30508924)
|
Project Period (FY) |
2012-04-01 – 2015-03-31
|
Project Status |
Completed (Fiscal Year 2014)
|
Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2013: ¥2,340,000 (Direct Cost: ¥1,800,000、Indirect Cost: ¥540,000)
Fiscal Year 2012: ¥2,080,000 (Direct Cost: ¥1,600,000、Indirect Cost: ¥480,000)
|
Keywords | 計量書誌分析 / 構造的関連性学習 / 機械学習 / 情報検索 / 学術情報 / テキストマイニング |
Outline of Final Research Achievements |
We propose a method to automatically associate documents from different domains such as scientific paper and patent. The proposed method enables cross-domain academic search on the scientific data. Borrowing ideas from multi-task learning and structural correspondence learning, our approach automatically identifies correspondences among the words from different domains using a small number of so-called concepts. Our method models the correlation between the concepts and all other words by training linear classifiers on the documents from different domains.
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Report
(4 results)
Research Products
(19 results)