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令和3年著作権法改正～なにが、どう変わる？～
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3年度第5回研究データ基盤運営委員会配布資料
Get PDF以下の資料は非公開である。
（資料2-2）【参考】第1回RDM人材育成作業部会議事要旨
（資料2-3）【参考】第2回RDM人材育成作業部会議事要旨
（資料2-4）【参考】第3回RDM人材育成作業部会議事要旨
（資料2-5）【参考】第4回RDM人材育成作業部会議事要旨
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。
（資料2-6）【参考】RDM支援_標準スキル_ver01_解説
（資料2-7）【参考】RDM支援_標準スキル_ver01_一覧
https://doi.org/10.20736/0002000219conference objec
Overview of NTCIR-16
Get PDFThis is an overview of NTCIR-16, the sixteenth sesquiannual research project for evaluating information access technologies. NTCIR-16 involved various evaluation tasks related to information retrieval, natural language processing, question answering, etc. 10 tasks were organized in NTCIR-16. This paper describes an outline of NTCIR-16, which includes its organization, schedule, scope, and task designs. In addition, we introduce brief statistics of the NTCIR-16 participants. Readers should refer to individual task overview papers for their detailed descriptions and findings.conference pape
WUT21 at the NTCIR-16 Data Search 2 Task
Get PDFThe WUT21 team participated in the IR subtask of the NTCIR-16 Data Search 2 Task. This paper reports our approach to solving the problem and discusses the official results. Our approach aims to choose a simple base model, for the IR subtask, using a document-based storage method to facilitate retrieval of specified fields, thereby formulating a retrieval strategy. Elastic Search is a distributed full-text search and analysis engine based on Lucene, which has the advantages of high performance, high scalability, and real-time performance. Based on Elastic Search, the strategy uses embedded retrieval algorithms to retrieve topics and calculate text similarity, and select the optimal algorithm to match topic texts according to the final evaluation index NDCG@10. The final results show that the basic text similarity algorithm has a relatively high contribution performance for information retrieval tasks.conference pape
Overview of the NTCIR-16 Dialogue Evaluation (DialEval-2) Task
Get PDFThis paper provides an overview of the NTCIR-16 Dialogue Evaluation (DialEval-2) task. DialEval-2 is the successor of The NTCIR-15 DialEval-1 task and the NTCIR-14 Short Text Conversation STC- 3 task. DialEval-2 consists of two subtasks: the Dialogue Quality (DQ) subtask and the Nugget Detection (ND) subtask. Both of the subtasks are designed to aim automatical evaluation of customerhelpdesk dialogues. The DQ subtask requires our participants to estimate three kinds of quality score for each dialogue: task accomplishment, customer satisfaction, and dialogue effectiveness. The ND subtask is set as a classification task, where participants are asked to classify every turn of a dialogue to detect nugget turns. A nugget stands for a turn being helpful for problem solving in the dialogue. In this paper, we introduce the task definition, data collection, evaluation measures, and the official evaluation results on the runs from the participant teams.conference pape
CYUT at the NTCIR-16 FinNum-3 Task: Data Resampling and Data Augmentation by Generation
Get PDFThis paper presents a description for our submission to the NTCIR-16 FinNum-3 shared task in fine-grained claim detection for financial documents. We submitted three runs in both the English and Chinese sections in the final test. The Run1 uses MacBERT (for Chinese data) and RoBERTa (for English data) with the classical classifier BiLSTM as the baseline of this study. In Run2, we change the classifier to AWD-LSTM for comparison. Furthermore, considering the the problem of unbalanced training data when training the model, we use data resampling technique in both Run1 and Run2. And we propose an attempt to extend the data using GPT2 in Run3.conference pape
Passau21 at the NTCIR-16 FinNum-3 Task: Prediction Of Numerical Claims in the Earnings Calls with Transfer Learning
Get PDFThe FinNum Task series aims at better understanding of numeral information in financial narratives. The goal of FinNum-3; on the English data part; is to have a fine-grained manager’s claim detection in the Earning Conference Calls (ECCs) with the help of Natural Language Processing (NLP). To succeed in the best performance for predicting in-claim and out-of-claim numerals, we propose the BERT (Bidirectional Encoder Representations from Transformers) base model , which is pre-trained on a large corpus of English data. The results of our model are 86.48% of macro-F1 score in the validation split and 87.12% of macro-F1 score in the test data.conference pape
Forst: A Challenge to the NTCIR-16 QA Lab-PoliInfo-3 Task
Get PDFIn this paper, we describe the development of a system for QA Alignment and a system for Fact Verification. We submitted 11 re- sults for the QA Alignment, 6 results including 4 late submissions for the Fact Verification. As a result, an F-measure of .7753 for the QA Alignment and an F-measure of .8563 for the Fact Verification were obtained.conference pape