NII Repository (National Institute of Informatics)
Not a member yet
2035 research outputs found
Sort by
TMUNLPG2 at the NTCIR-18 MedNLP-CHAT Task
Full text linkThe TMUNLPG2 team participated in the Japanese subtask of the NTCIR-18 Medical Natural Language Processing for AI Chat (MedNLP-CHAT) Task. This paper presents our methodological approach and analyzes the official results. For the Japanese subtask, we implemented two distinct methodologies addressing the objective and subjective components. In the objective task, we fine-tuned a pre-trained language model enhanced with focal loss, comprehensive feature engineering, and strategic data augmentation techniques to optimize performance. For the subjective task, we developed specialized feature engineering methods to extract implicit semantic relationships within question-answer pairs, subsequently leveraging these features to train a robust deep learning architecture. Our approach yielded significant results, with TMUNLPG2 achieving the highest average F1-score among seven participating teams in the objective task and securing second place in the subjective task. These outcomes demonstrate the efficacy of our methodological framework and highlight its potential applications in advancing medical natural language processing systems.conference pape
Hirosaki team at the NTCIR-18 RadNLP2024 Shared Task: Few-Shot Learning and Prompt Engineering for TNM Staging Classification of English Radiology Reports Using Large Language Models.
Full text linkWe participated in the NTCIR-18 RadNLP2024 shared task [1] and investigated the automation of TNM classification using large language models (LLMs), specifically GPT-4o-mini, GPT-4o, and o1-mini. Our approach integrates cosine similarity-based retrieval using embedding vectors and few-shot learning to enhance classification accuracy. As a result of the experiment, o1-mini achieved the highest classification accuracy. However, the accuracy on the test data declined by approximately 30% compared to the validation data. In particular, the low classification accuracy of the T factor highlighted challenges in interpreting tumor size and extent of infiltration. In this paper, we analyze these results and report our approach to this task along with official results.conference pape
TMUNLPG3 at the NTCIR-18 RadNLP Task
Full text linkThe TMUNLPG3 team participated in the Lung Cancer Staging main task and Multi-label Sentence Classification subtask of the NTCIR-18 RadNLP Task. This paper illustrates our approach to address the challenges and discusses the official results. We tackled Lung Cancer TNM Staging maintask to highest among all participants in the English track by adopting LLM and Few-Shot prompt engineering. Our solution also performed excellently in the Multi-label Sentence Classification subtask.conference pape
Overview of the NTCIR-18 HIDDEN-RAD Task: Hidden Causality Inclusion in Radiology Report Generation
Full text linkThe Hidden-Rad task, introduced as a pilot challenge at NTCIR18, aims to improve the interpretability of AI systems in radiologyrelated diagnostic reasoning by encouraging models to explicitly explain the rationale behind clinical interpretations. Traditional radiology reports often focus on final diagnoses while omitting the underlying causal reasoning. To address this, Hidden-Rad defines two subtasks: Task 1 targets diagnostic explanation generation using radiology reports, with optional use of X-ray images; Task 2 evaluates the interpretation of diagnostic reasoning from structured clinical questionnaires. The task is built on an enriched subset of the MIMIC-CXR dataset and includes formal evaluation criteria provided via a public repository. In total, three teams submitted 40 runs for Task 1, while two teams submitted 16 runs for Task 2. The top-performing systems achieved 69% and 78.84% for each subtask, respectively, demonstrating the potential for integrating causal reasoning into clinical report generation. The findings highlight future directions for explainable medical AI through the use of domain-specific knowledge graphs and customized language models.conference pape
Evaluating Group Fairness and Relevance in Conversational Search: An Alternative Formulation
Full text linkThe Conversational Search (CS) Subtask of the
NTCIR-18 FairWeb-2 Task used Sakai's
GFRC (Group Fairness and Relevance for Conversations)
measure
for evaluating the participating systems.
As the Relevance and Group Fairness components were
not directly integrated in GFRC and the measure lacked a
clear user model,
the present pilot study discusses an alternative called
GFRC2.
By directly transferring the general idea of
the GFR (Group Fairness and Relevance) framework for web
search
to the task of evaluating generated conversations,
we formulate GFRC2 as a form of expected user experience
for a population of users who go through the words within
the conversation.
This also lets us visualise the Relevance and Group Fairness
component scores for each cluster of users
who are assumed to abandon the conversation
at a particular relevant nugget.
We demonstrate
the steps of computing GFRC2 using
real runs from the FairWeb-2 CS Subtask.conference pape
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」 オープンアクセス時代の情報リテラシー 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
2024年度国立情報学研究所実務研修報告
Full text link研修名：2024年度実務研修
開催期間：2024年4月1日（月）～2025年3月31日（月）
主催：国立情報学研究所conference presentatio
ダイ 44 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 ギジヨウシ
Full text link会議名：第44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10月30日（水）10:00～12:00conference outpu
ダイ 42 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 ギジヨウシ
Full text link会議名：第42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1月24日（水）15:00～17:00conference outpu