Research on Knowledge Extraction from Ancient Mongolian Historical Documents using Deep Learning
| Project/Area Number |
17K00457
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Library and information science/Humanistic social informatics
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| Research Institution | Ritsumeikan University |
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Principal Investigator |
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| Project Period (FY) |
2017-04-01 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| Budget Amount *help |
¥4,420,000 (Direct Cost: ¥3,400,000、Indirect Cost: ¥1,020,000)
Fiscal Year 2020: ¥780,000 (Direct Cost: ¥600,000、Indirect Cost: ¥180,000)
Fiscal Year 2019: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2018: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
Fiscal Year 2017: ¥1,170,000 (Direct Cost: ¥900,000、Indirect Cost: ¥270,000)
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| Keywords | deep learning / historical documents / traditional Mongolian / machine learning / named entity extraction / deep learming / Deep learning / 情報検索 / 情報図書館学 / 情報システム |
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| Outline of Final Research Achievements |
In this research, an information extraction and analysis method for digitized ancient Mongolian historical documents is proposed. The proposed method extracts features from historical manuscripts by utilizing deep learning techniques. Extracted deep features are utilized for building retrieval systems that encode the interpretations of ancient Mongolian words, as well as features for recognizing different letters with the same shape. Digital representations of ancient historical manuscripts with annotated ancient Mongolian texts along with the scanned images of manuscripts could be used as scholarly tools. The proposed methods were applied not only to Mongolian historical manuscripts but also to Oracle bone script and Ukiyo-e, a Japanese traditional art.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は歴史的文書の分析にかかる時間と手間を軽減することができると考えられる。現代モンゴル語の文書には含まれない隠れた知識を伝統的モンゴル文字の古文書から発見できると考えられる。提案方法を用いた歴史的文書の分析結果を分かりやすく表示するシステムはどんなユーザーにも利益をもたらすことが期待されている。さらに、歴史書類のデジタル化を研究対象にしている学者に大きく貢献すると考えられる。
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Report
(5 results)
Research Products
(34 results)
