Research Project
Grant-in-Aid for Young Scientists (B)
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.
All 2014 2013 2012 Other
All Journal Article (3 results) (of which Peer Reviewed: 3 results, Open Access: 2 results) Presentation (13 results) Remarks (3 results)
Scientometrics
Volume: 101 Issue: 2 Pages: 1515-1533
10.1007/s11192-014-1380-x
Journal of Engineering and Technology Management
Volume: 32 Pages: 129-146
10.1016/j.jengtecman.2013.07.002
日本ロボット学会誌
Volume: Vol.31 No.8 Pages: 804-815
http://academic-landscape.com
http://academic-landscape.com/