NII Repository (National Institute of Informatics)
Not a member yet
2035 research outputs found
Sort by
Annual Report 2024
Full text link2024年度 国立情報学研究所年報
目次
はじめに
1．概要
（1） 沿革 1
（2） 組織 5
2．研究
（1） 研究活動の総括 8
（2）共同研究 36
（3）科学研究費助成事業による研究 40
（4）受託研究 43
（5）受託事業 47
（6） 個人研究業績 48
（7） 奨学寄附金 159
3．教育
（1） 総合研究大学院大学先端学術院情報学コース 160
（2） 他大学院教育への協力 167
4．事業4-1.学術情報基盤整備活動
（1） 学術情報基盤整備活動の概要 172
（2） 学術情報ネットワーク（SINET6）174
（3） 学術認証基盤の構築 178
（4） クラウドの導入・活用支援 179
（5） 学術コンテンツ基盤の整備 180
（6） 教育研修事業 186
（7） 大学図書館コンソーシアム連合（JUSTICE） 188
（8） オープンアクセスリポジトリ推進協会（JPCOAR） 189
4-2.大学間連携に基づく情報セキュリティ体制の基盤構築 190
5．国際交流 191
6．広報・普及 206
7．知的財産 218
8．社会連携 219
9．NII CSIRT 220
10．図書室 221
11．決算 223
12．外部資金 224
13．施設 225
14．会議等
（1）国際戦略アドバイザー 227
（2）アドバイザリーボード 227
（3）運営会議 227
（4）各種委員会 229
（5）事業関連委員会 248
15．記録
（1） 人事異動 257
（2） 表彰・受賞 263
（3） 活動記録 266
索引 272othe
NCR2018対応に伴う主な変更点について
Full text link研修名：2025年度目録システム書誌作成研修
開催日：2025年9月18日（木）、9月19日（金）、11月28日（金）
主催：国立情報学研究所conference presentatio
Overview of the NTCIR-18 FinArg-2 Task: Temporal Inference of Financial Arguments
Full text linkThis paper provides an overview of the FinArg-2 shared tasks in NTCIR-18. Building upon the fundamental argument identification tasks in FinArg-1, this iteration focuses on temporal inference. Forward-looking statements frequently appear in financial documents, and we aim to capture the duration of a premise's impact on a company's operations, the temporal reference associated with an argument, and the validity period of a claim. Similar to FinArg-1, we utilize earnings conference calls, professional research reports, and social media data for analysis. A total of 20 teams registered for FinArg-2, with 7 active teams submitting their results. \textcolor{red}{We will highlight some methods after receiving participants' submissions.}conference pape
QshuNLP's Participation in the SUSHI: Systems and Analysis of the Results
Full text linkKyushu University's team (QshuNLP) participated the both subtasks of the NTCIR-18 SUSHI pilot task. In this paper, we describe our approaches, systems, and analyze the results.conference pape
ダイ 43 カイ コレカラ ノ ガクジュツ ジョウホウ システム コウチク ケントウ イインカイ ハイフシリョウ
Full text link会議名：第43回 これからの学術情報システム構築検討委員会
開催場所：オンライン
日時：2025年6月25日（水）13:00～15:00conference outpu
LLM-Based Two-stage Reasoning for TNM Staging: NECMedDX at the NTCIR-18 RadNLP Task
Full text linkWe propose a novel method for automatically inferring TNM stages from radiology reports. The proposed method includes a two-stage reasoning process. In Stage 1, kNN few-shot learning with the Chain of Thought is used for initial inference, followed by a self-review to evaluate the reasoning process. In Stage 2, if the inference results after the self-review are inconsistent, a second review is conducted from an alternative perspective. The proposed method achieved superior results in the NTCIR-18 RadNLP 2024 Main Task (Japanese), outperforming other teams by approximately 7.4 points, thereby winning the competition. The proposed method is designed as an extension of prompt engineering. It requires no complex training, which makes it applicable to various large language models.conference pape
デジタルアーカイブ カケル メタデータ ベンキョウカイ 5
Full text link名称：デジタルアーカイブ×メタデータ勉強会 #5
開催場所：オンライン
日時：2025年3月5日（水）10:30～12:00conference presentatio
IITUH18 at Fairweb-2: Investigating the Effect of the Query Modification on Fairness
Full text linkAs information retrieval systems become increasingly sophisticated, ensuring fairness and algorithmic neutrality in search results has emerged as a critical challenge. Traditional ranking algorithms often prioritize relevance, which can unintentionally amplify the visibil- ity of majority groups while limiting representation for minority perspectives. This imbalance can lead to biased search results that reinforce existing disparities. To address this issue, fairness-aware retrieval methods aim to ensure equitable representation by balanc- ing relevance with exposure fairness while maintaining algorithmic neutrality. In this study, we investigate the impact of query modifi- cations on group fairness in ranked search results. Specifically, we examine how expanding queries to encompass a broader range of relevant content influences fairness between different groups while considering their protected attributes. Our findings contribute to ongoing efforts to design information retrieval systems that provide more inclusive and bias free access to information.conference pape
TMULLA at the NTCIR-18 MedNLP-CHAT Task
Full text linkThe NTCIR-18 MedNLP-CHAT RISK task evaluates the potential medical, ethical, and legal risks posed by chatbot-generated responses to patient inquiries. This study investigates a sentence-level risk classification approach to identify specific sentences within chatbot responses that contribute to risk assessment rather than treating entire responses as monolithic risk units. Our methodology involved automatic sentence segmentation, contextual risk annotation, and threshold-based classification, leveraging traditional natural language processing (NLP) models instead of large language models (LLMs) to ensure interpretability and stability. Despite the conceptual validity of our approach, our system did not perform competitively, particularly in ethical and legal risk classification. A key limitation was using a single model for all risk types, which failed to capture the nuanced distinctions between medical, ethical, and legal risk factors. Additionally, dataset constraints and class imbalance (fewer than 30 positive samples per risk category) limited model generalization. While sentence-level annotation improved granularity, it introduced challenges in handling cross-sentence risk dependencies, where risks emerge from multi-sentence interactions rather than isolated statements. Our findings highlight the need for more advanced risk classification frameworks, incorporating sequence-aware models, domain-specific fine-tuning, and context-sensitive risk evaluation. We also discuss the cultural relativity of risk perception, emphasizing that risk assessments should account for jurisdictional differences in medical, legal, and ethical norms. Future research should explore hybrid NLP architectures, data augmentation techniques, and adaptive risk modeling to enhance chatbot safety and reliability in medical AI applications.conference pape