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第1回 SPARC Japan セミナー2020 「研究データ公開：フルオープンと制限公開の境界線」 材料分野の研究データ公開における理想と課題～NIMSの事例から～ ドキュメント
Get PDF第1回 SPARC Japan セミナー2020 「研究データ公開：フルオープンと制限公開の境界線」
開催場所：オンライン開催
日時：2020年10月2日（金）13:00-17:00conference objec
SPARC Japan セミナー2021 「研究データポリシーが目指すものとは」 総合討論（第1部）研究データポリシーをめぐる論点整理を中心に ドキュメント
Get PDFSPARC Japan セミナー2021「研究データポリシーが目指すものとは」
開催場所：オンライン開催
日時：2022年2月22日（火）13:00-16:55conference objec
大学図書館における目録実務とその周辺
Get PDF研修名：2022年度大学図書館職員短期研修
開催期間：2022年10月18日（火）～10月21日（金）
主催：東京大学附属図書館、京都大学附属図書館、国立情報学研究所othe
The Impact of Query Variability and Relevance Measurement Scales on Information Retrieval Evaluation
Get PDFInformation retrieval makes extensive use of test collections for the measurement of search system effectiveness. Broadly speaking, this evaluation framework includes three components: search queries; a collection of documents to search over; and relevance judgements. In this talk, we'll consider two aspects of this process: queries, and relevance scales. Test collections typically use a single query to represent a more complex search topic or information need. However, different people may generate a wide range of query variants when instantiating information needs. We'll consider the implications of this for the evaluation of search systems, and the potential benefits and costs of incorporating variant queries into a test collection framework. Relevance judgements are used to indicate whether the documents returned by a retrieval system are appropriate responses for the query. They can be made using a variety of different scales, including ordinal (binary or graded) and techniques such as magnitude estimation. We'll examine a number of different approaches, and explore their benefits and drawbacks for judging relevance for retrieval evaluation.conference pape
DCU at the NTCIR16 Lifelog-4 Task
Get PDFIn this paper, we present two systems from DCU named DCUMemento and DCUVOX that earlier participated in the 2021 edition of the Lifelog Search Challenge and were redeveloped to participate in the NTCIR-16 Lifelog-4 task. Both systems use image-text embeddings from various CLIP models to build their search backend with DCUVOX using the ViT-B/32 model while DCUMemento uses a weighted ensemble of scores from ViT-L/14 and ResNet-50x64 models. The paper also discusses the query reformulation strategy used by the systems in addition to the system architecture. Finally, we present the results of our evaluation and discuss limitations of both systems with details of improvements planned for future iterations.conference pape
Leveraging Token-Based Concept Information and Data Augmentation in Few-Resource NER: ZuKyo-EN at the NTCIR-16 Real-MedNLP task
Get PDFIn this paper, we discuss our contribution to the NII Testbeds and Community for Information Access Research (NTCIR) - 16 Real-MedNLP shared task. Our team (ZuKyo) participated in the English subtask: Few-resource Named Entity Recognition. The main challenge in this low-resource task was a low number of training documents annotated with a high number of tags and attributes. For our submissions, we used different general and domain-specific transfer learning approaches in combination with multiple data augmentation methods. In addition, we experimented with models enriched with biomedical concepts encoded as token-based input features.conference pape
人文学における学術情報の流通
Get PDF2022年5月31日 学術情報基盤オープンフォーラム2022, コンテンツトラック1「研究データ流通のベストプラクティスと学術基盤の役割」conference objec