Cross-Domain Academic Search using Structural Correspondence Learning

2014 Fiscal Year Final Research Report

• Project/Area Number: 24700137
• Research Category: Grant-in-Aid for Young Scientists (B)
• Allocation Type: Multi-year Fund
• Research Field: Intelligent informatics
• 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
• Keywords: 計量書誌分析 / 構造的関連性学習 / 機械学習 / 情報検索

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.

Free Research Field

人工知能