| 研究課題/領域番号 |
20K20135
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| 研究種目 |
若手研究
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| 配分区分 | 基金 |
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| 審査区分 |
小区分90020:図書館情報学および人文社会情報学関連
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| 研究機関 | 立命館大学 |
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研究代表者 |
SONG Yuting 立命館大学, 情報理工学部, 助教 (50849388)
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| 研究期間 (年度) |
2020-04-01 – 2022-03-31
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| 研究課題ステータス |
中途終了 (2021年度)
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| 配分額 *注記 |
4,160千円 (直接経費: 3,200千円、間接経費: 960千円)
2022年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
2021年度: 1,300千円 (直接経費: 1,000千円、間接経費: 300千円)
2020年度: 1,430千円 (直接経費: 1,100千円、間接経費: 330千円)
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| キーワード | Entity matching / MT Evaluation / Entity recognition / Relation extraction / Word embeddings / MT evaluation / Metadata translation / Knowledge extraction / Cultural collections / Multilingual information |
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| 研究開始時の研究の概要 |
Recently, many cultural institutions have been making their cultural collections accessible through their metadata. However, multilingual knowledge in digital collections is less considered for accessing these collections.
This research aims to extract multilingual knowledge from metadata, including entities and object relations, by utilizing neural network based techniques of entity extraction and representation learning. The extracted knowledge can be applied to improve multilingual information access to digital cultural collections and help people understanding digital cultural objects.
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| 研究実績の概要 |
This year we focused on improving the method of cross-lingual entity matching and collecting datasets for machine translation evaluation.
First, we proposed a novel method to identify records that refer to the same Japanese artwork entity in Japanese and English data sources. Our approach considered an entity as a sequence of attributes and employed a multilingual BERT-based network to enable cross-lingual entities to be compared without aligning the schema. In addition, we collected datasets and conducted further experiments to evaluate machine translations on translating ukiyo-e metadata records, especially the genre of bijin-e. In another work, we have investigated and evaluated the current state-of-the-art models to automatically discover entities and relations in short texts.
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