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SPARC Japan セミナー2024 「オープンアクセス義務化の先にあるもの：来るべき世界に向けて」 オープンサイエンス実装社会で活きるブリッジコミュニケーション ドキュメント
Full text linkSPARC Japan セミナー2024「オープンアクセス義務化の先にあるもの：来るべき世界に向けて」
開催場所：オンライン開催
日時：2025年1月30日（木）13:00～17:00conference presentatio
はじめてのSQL－SQL入門－
Full text link研修名：2025年度大学図書館員のためのIT総合研修
開催期間：2025年8月20日（水）～8月22日（金）
主催：国立情報学研究所conference presentatio
RSLFW at the NTCIR-18 FairWeb-2 Task
Full text linkThis report presents the participation of RSLFW team at the NTCIR-18 FairWeb-2 task.We implemented several different retrieval methods to generate five runs using BM25, ColBERT and PM-2 algorithm.In addition to the runs submitted, the results are analyzed through comparison with the official baseline and FairWeb-1 reproduction (revived) runs.conference pape
TMAK at NTCIR-18 FinArg-2 Task
Full text linkThis paper presents our participation in FinArg-2, which succceeds the FinArg-1 task. While FinArg-1 focused on sentiment analysis and argument classification, FinArg-2 extends this to temporal. We experiment with a method of classifying text into two types: "Premise" and "Claim." Based on these premises and claim, we have developed a method suitable for accurately classifying the temporal relationships between sentences. In order to classify sentences, we trained a classification model on labeled data, and compared traditional machine learning approaches with models that use large scale language models. Among the models tested, DeBERTa and Llama achieved the highest classification accuracy, demonstrating the model that used a large-scale language model showed auperior results.conference pape
Overview of the NTCIR-18 Lifelog-6 Task
Full text linkNTCIR-18 marked the sixth iteration of the Lifelog task, which aims to advance research on multimodal lifelog organization, search, and access. This task builds on methodologies successfully deployed in previous NTCIR conferences. In this paper, we detail the test collection, outline the specific tasks, provide an overview of submissions, and present findings from the NTCIR-18 Lifelog-6 task. We conclude with recommendations for future developments in lifelog research.conference pape
TUSNLP at the NTCIR-18 MedNLP-CHAT Task: Utilization of External Medical Knowledge and Hybrid Approach of BERT and ChatGPT
Full text linkWe developed model systems for detecting medical, legal, and ethical risks in medical chatbot answers by using BERT and ChatGPT language models. The ChatGPT model system, which refers to external medical knowledge, performed best in detecting medical risk, while the BERT model system performed well in detecting legal and ethical risks. The hybrid model system reduces missed risks by combining the best of the BERT and ChatGPT model systems and has the best recall values for all risk determination models. This study demonstrates the usefulness of utilizing external medical knowledge and the effectiveness of the hybrid approach.conference pape
AITOK at the NTCIR-18 MedNLP-CHAT to Identify Medical, Ethical and Legal Risks in Patient-Doctor Conversations
Full text linkArtificial intelligence (AI) is rapidly transforming many fields, and healthcare is no exception. The current state of AI in healthcare is characterized by a shift toward addressing ethical concerns and developing a robust framework for AI integration. Generative AI, a subset of AI that includes Large Language Models (LLMs), has emerged as a game changer with the potential to revolutionize medical consultations. Therefore, the AITOK team participated in Japanese/German subtasks of the NTCIR-18 MedNLP-CHAT using statistical knowledge only, GPT-3.5 Turbo, and GPT-4o, respectively. This report describes the problem-solving approach using generative AI for medical, legal, and ethical issues in medical consultation and its formal results.conference pape
SINAI team at NTCIR-18 RadNLP 2024
Full text linkThis paper presents our participation in the NTCIR-18 RadNLP 2024 English main task and subtask. We describe our proposed solution to address the problem and discuss the official results. Our approach is based on large language models, with additional experiments involving data augmentation, retrieval-augmented generation, and prompting for the main task. Additionally, for the subtask, we employed a ModernBERT model with pre-training and hyperparameter optimization. Our best-performing submission in the main task, scores 0.5309\% in overall joint accuracy (fine) evaluation. Also, our best-performing submission in the subtask, scores 0.8189\% in overall micro F2.0 evaluation. Results from additional runs also show that data augmentation could further improve model performance beyond our best submission.conference pape
SOCIOCOM at the NTCIR-18 RadNLP Main task: Zero-Shot LLM Approaches for Lung Cancer Staging
Full text linkThis paper describes our approach to the RadNLP 2024 Maintask as participants of NTCIR-18. The RADNLP 2024 Main Task is to classify the stage of lung cancer from radiology reports. Our approach utilizes GPT-4o for inference, employing prompt engineering techniques. We achieved an accuracy of 0.5648 on the Japanese test data, demonstrating the robustness of closed-source models.conference pape
目指すは知識活用基盤 －情報メディアの50年を15分で振り返りながら－
Full text link会議名：学術情報基盤オープンフォーラム2025
開催場所：CiNii Researchトラック「これからどうなる？CiNii Research
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日時：2025年6月16日（月）～6月18日（水）conference outpu