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UEM24 at the NTCIR-18 MedNLP-CHAT: A Machine Learning Approach to Multilingual Healthcare Risk Prediction
Full text linkRisk prediction in the context of medical, ethical, and legal is crucial for ensuring safety and informed decision-making. This study explores machine learning approaches for the MedNLP-CHAT task, utilizing English-translated datasets from Japanese and German subtasks. The textual data underwent preprocessing, including tokenization, n-gram extraction, and lemmatization, before being modeled using Logistic Regression, Nu-SVC (nu=0.1) [2], Gradient Boosting, and XGB Regressor. Objective risks were framed as a binary classification task, while subjective labels were predicted via regression, ensuring alignment with human-annotated distributions. Performance was evaluated using accuracy, precision, recall, F1-score, and Earth Mover’s Distance (EMD). The findings indicate the model’s strengths and weaknesses, emphasizing the need to enhance how class imbalances and potential overfitting are addressed. This work increases AI-driven risk assessment with applications in regulatory compliance, healthcare, and ethical AI development.conference pape
FUSINT at the NTCIR-18 U4 Task
Full text linkThis paper describes the proposed methods and results of the FUSINT team in the U4 task. For the Table Retrieval task, we propose a method for retrieving specific tables in Securities Reports based on a given question. Our approach involves filtering using cosine similarity and reranking, followed by a binary classification model. We achieved approximately 90% accuracy, but challenges remain in preprocessing and generalizing the section prediction model. Future work should explore methods that can handle a wider variety of question formats. For the Table QA task, we propose a method for identifying table cells in Securities Reports, focusing on standardizing table structures and resolving inconsistencies in cell values. One advantage of our approach is its ability to visualize the reasoning process. While challenges remain in handling hierarchical tables due to matrix segmentation, our method successfully identified cell positions with a high accuracy of approximately 92%.conference pape
電子リソースデータ共有サービス（NII-DEER)の最新動向
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
LifeIR at the NTCIR-18 Lifelog-6 Task
Full text linkIn recent years, sharing lifelogs recorded through wearable devices such as sports watches and GoPros, has gained significant popularity. Lifelogs involve various types of information, including images, videos, and GPS data, revealing users' lifestyles, dietary patterns, and physical activities. The Lifelog Semantic Access Task(LSAT) in the NTCIR-18 Lifelog-6 Challenge focuses on retrieving relevant images from a large scale of users' lifelogs based on textual queries describing an action or event. It serves users' need to find images about a scenario in the historical moments of their lifelogs. We propose a multi-stage pipeline for this task of searching images with texts, addressing various challenges in lifelog retrieval. Our pipeline includes: filtering blurred images, rewriting queries to make intents clearer, extending the candidate set based on events to include images with temporal connections, and reranking results using a multimodal large language model(MLLM) with stronger relevance judgment capabilities. The evaluation results of our submissions have shown the effectiveness of each stage and the entire pipeline.conference pape
LLM-based Relevance Assessment Still Can’t Replace Human Relevance Assessment
Full text linkThe use of large language models (LLMs) for relevance
assessmentin information retrieval has gained significant
attention, with recent studies suggesting that LLM-based
judgments provide comparable evaluations to human
judgments. Notably, based on TREC2024 data, Upadhyay et al.
(2024) make a bold claim that LLM-basedrelevance
assessments, such as those generated by the Umbrelasystem,
can fully replace traditional human relevance assessmentsin
TREC-style evaluations. This paper critically examines this
claim,highlighting practical and theoretical limitations
that underminethe validity of this conclusion.First, we
question whether the evidence provided by Upadhyayet al.
genuinely supports their claim, particularly when the
testcollection is intended to serve as a benchmark for
future researchinnovations. Second, we submit a system
deliberately crafted toexploit automatic evaluation
metrics, demonstrating that it canachieve artificially
inflated scores without truly improving retrievalquality.
Third, we simulate the consequences of circularity by
analyzing Kendall’s tau correlations under the hypothetical
scenarioin which all systems adopt Umbrela as a final-stage
re-ranker,illustrating how reliance on LLM-based
assessments can distortsystem rankings. Theoretical
challenges – including the inherentnarcissism of LLMs, the
risk of overfitting to LLM-based metrics,and the potential
degradation of future LLM performance – thatmust be
addressed before LLM-based relevance assessments can
beconsidered a viable replacement for human judgments.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」 開会挨拶/概要説明 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
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」 知の循環のミッシングリンク：知的資産はどのような利用事例を生み出すか？ 発表資料
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