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ResourceSync Framework Specification - Framework Notification
Get PDF本アイテムは「ResourceSync Framework Specification - Framework Notification」を国立情報学研究所で翻訳したものである。othe
効果的なグループワークのデザインとファシリテーション
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
令和4年度第2回研究データ基盤運営委員会配布資料
Get PDF以下の資料はオリジナルが公開されています。以下のURLよりご参照ください。
（参考資料2）オープンサイエンスのためのデータ管理基盤ハンドブック
https://doi.org/10.20736/0002000318conference objec
Evaluating Evaluation Measures, Evaluating Information Access Systems, Designing and Constructing Test Collections, and Evaluating Again
Get PDFI plan to cover the following topics in this tutorial: 1. Why is (offline) evaluation important? 2. On a few evaluation measures used at NTCIR 3. How should we choose the evaluation measures? 4. How should we design and build a test collection? 5. How should we ensure the quality of the gold data? 6. How should we report the results? 7. Quantifying reproducibility and progress 8. Summaryconference pape
JRIRD at the NTCIR-16 FinNum-3 Task: Investigating the Effect of Numerical Representations in Manager's Claim Detection
Get PDFThis study presents JRIRD's work on the FinNum-3 Manager's Claim Detection subtask. Numeracy is essential in financial documents and some studies have focused on numerical information representations in natural language processing. For the FinNum-3 task, we tried four representations of numerical value in a text and experimented with joint learning using numerical category information. The results showed that the best format of numerical values depended on a pre-trained model. The joint learning with numerical categories improved the performance of some pre-trained models and numeral format settings.conference pape
IMNTPU at the NTCIR-16 FinNum-3 Task: Data Augmentation for Financial Numclaim Classification
Get PDFThis paper provides a detailed description of IMNTPU team at the NTCIR-16 FinNum-3 shared task in formal financial documents. We proposed the use of the XLM-RoBERTa-based model with two different approaches on data augmentation to perform the binary classification task in FinNum-3. The first run (i.e., IMNTPU-1) is our baseline through the fine-tuning of the XLM-RoBERTa without data augmentation. However, we assume that presenting different data augmentations may improve the task performance because of the imbalance in the dataset. Accordingly, we presented double redaction and translation methods on data augmentation in the second (IMNTPU-2) and third (IMNTPU- 3) runs, respectively. The best macro-F1 scores obtained by our team in the Chinese and English datasets are 93.18% and 89.86%, respectively. The major contribution of this study provides a new understanding of data augmentation approach for the imbalanced dataset, which may help reduce the imbalanced situation in the Chinese and English datasets.conference pape
Overview of the NTCIR-16 Lifelog-4 Task
Get PDFNTCIR-16 saw the fourth edition of the Lifelog task, which aimed to foster comparative benchmarking of approaches to automatic and interactive information retrieval from multimodal lifelog archives. In this paper, we describe the test collection employed, along with the tasks, the submissions and the findings from this NTCIR16 Lifelog-4 LEST sub-task. We finish by suggesting future plans for lifelog tasks.conference pape
JRIRD at the NTCIR-16 QA Lab-PoliInfo-3 Budget Argument Mining
Get PDFThe JRIRD team participated in the budget argument mining subtask of the NTCIR-16 QA Lab-PoliInfo-3. This paper reports on our approach to solving this problem and discusses the official results. Our system consists of two BERT models that work independently toward two objectives: argument classification (AC) and related ID detection (RID). The results show that our system performs well, especially for argument classification.conference pape
KASYS at the NTCIR-16 WWW-4 Task
Get PDFThe KASYS team participated in the English subtask of the NTCIR-16 WWW-4 task. This paper describes our approach of generating NEW runs, and REV runs in the NTCIR-16 WWW-4 task. We applied BERT reading comprehension model to the WWW-4 task for generating NEW runs. We investigated the effectiveness of reading comprehension model in the ad-hoc Web document retrieval task. The evaluation results showed that our run outperformed the baseline in the gold relevance assessment for the four runs we submitted. The evaluation results of REV runs showed that our runs in WWW-3 still well performed in WWW-4.conference pape