NII Repository (National Institute of Informatics)
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SPARC Japan セミナー2023 「即時OAに備えて：論文・データを「つかってもらう」ためのライセンス再入門」 J-STAGE Dataの現状とライセンスについて 発表資料
Get PDFSPARC Japan セミナー2023「即時OAに備えて：論文・データを「つかってもらう」ためのライセンス再入門」
開催場所：オンライン開催
日時：2023年11月28日（火）13:00-17:00conference objec
SPARC Japan Seminar 2023 "Preparing for Immediate OA: A Reintroduction to Licensing for Getting Your Papers and Data Used" Open Science:Publisher perspectives on open access and licensing Presentation Material
Get PDFSPARC Japan セミナー2023「即時OAに備えて：論文・データを「つかってもらう」ためのライセンス再入門」
開催場所：オンライン開催
日時：2023年11月28日（火）13:00-17:00conference objec
SPARC Japan セミナー2023 「即時OAに備えて：論文・データを「つかってもらう」ためのライセンス再入門」 開会挨拶/概要説明 ドキュメント
Get PDFSPARC Japan セミナー2023「即時OAに備えて：論文・データを「つかってもらう」ためのライセンス再入門」
開催場所：オンライン開催
日時：2023年11月28日（火）13:00～17:00conference objec
MemoriEase at the NTCIR-17 Lifelog-5 Task
Get PDFThis paper presents the MemoriEase retrieval system that participated in the NTCIR Lifelog-5 Task. We report our method to solve the lifelog retrieval problem and discuss the official results of the MemoriEase at Lifelog-5 task. The MemoriEase system was originally introduced in the Lifelog Search Challenge (LSC) as an interactive lifelog retrieval system and it is modified to an automatic retrieval system to address the NTCIR Lifelog-5 Task. We propose the BLIP-2 model as the core embedding model to retrieve lifelog images from textual queries. The open-sourced Elasticsearch search engine serves as the main engine in the MemoriEase system. Some pre-processing and post-processing techniques are applied to adapt this system to an automatic version and improve the accuracy of retrieval results. Finally, we discuss the result of the system on the task, some limitations of the system, and lessons learned from participating in the Lifelog-5 task for further improvements for the system in the future.conference pape
fuys Team at the NTCIR-17 QA Lab-PoliInfo-4 Task
Get PDFThis paper reports on the fuys team's NTCIR-17 QA Lab-PoliInfo-4 Minutes-to-Budget Linking (MBLink) results. We thought that related tables could be found by focusing on the cells of the table. Learning inferences were made by combining the text of tag with an ID and the text of table cell. The two were encoded and combined to perform a binary classification. We considered a table relevant if there was at least one related word in the table's cells. We also tried this when the text of a table cell was joined column by column and combined with the text of a tag with an ID. The best accuracy was obtained when the text in table cells was joined column by column.conference pape