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OUC at NTCIR-17 UFO TDE and TTRE
Get PDFThe OUC team participated in the Table Data Extraction (TDE) subtask and the Text-to-Table Relationship Extraction (TTRE) of NTCIR-17 Understanding of Non-Financial Objects in Financial Reports (UFO). In this paper, we report our methodology in this task and discuss the official results.conference pape
NAISTSOCRR at the NTCIR-17 MedNLP-SC Radiology Report Subtask
Get PDFThis paper describes how we tackled the Medical Natural Language Processing for Radiology Report TNM staging (RR-TNM) Subtask as participants of NTCIR17. The RR-TNM Subtask is a MedNLP-SC original task to classify radiology reports under multiple criteria. We introduced three different methods based on pre-trained language models (PLMs), including a medical-specific model. Notably, our combination approach, utilizing JMedRoBERTa (manbyo-wordpiece) for label T, Tohoku-BERT-v3 for label N, and UTH-BERT for label M, achieved an accuracy of 0.3704 on the test data. This performance was the highest among all participants, emphasizing the effectiveness of our strategy.conference pape
SCUNLP-2 at the NTCIR-17 FinArg-1 Task: Enhancing Argumentative Relationship Recognition in the Classification Model with Language Generation Model Prompts
Get PDFWhile argument mining has significantly advanced across various domains, its application to financial discussions remains relatively unexplored. Our motivation for this research is rooted in the understanding that sentiment analysis alone may be inadequate when evaluating financial discussions, as the financial world is influenced by many factors intricately intertwined with the sentiments and opinions expressed by investors, analysts, and policymakers. To enhance the analysis of financial arguments, we incorporate GPT into the field of financial argument mining and design custom prompts. This unique integration allows us to generate labels and summaries for the arguments extracted from social media discussions. Our research results indicate that adding the generated labels in the regular mode achieved the highest validation set Marco-F1 score (66.39%). These findings contribute to a deeper understanding of argument mining in financial and social media discussions.conference pape
Overview of the NTCIR-17 Transfer Task
Get PDFThis paper provides an overview of the NTCIR-17 Transfer task, a pilot task that aims to bring together researchers from Information Retrieval, Machine Learning, and Natural Language Processing to develop a suite of technology for transferring resources generated for one purpose to another in the context of dense retrieval on Japanese texts. Two subtasks were proposed for this round: the Dense First Stage Retrieval subtask and the Dense Reranking subtask. We received 29 runs for the First Stage Retrieval and 25 runs for the Reranking subtask from three research groups. The evaluation results of these runs are presented and discussed in this paper.conference pape
Overview of the NTCIR-17 QA Lab-PoliInfo-4 Task
Get PDFThe goal of the NTCIR-17 QA Lab-PoliInfo-4 task is to develop real-world complex question answering (QA) techniques using Japanese political information such as local assembly minutes and newsletters. QA Lab-PoliInfo-4 consists of four subtasks: Question Answering-2, Answer Verification, Stance Classification-2, and Minutes-to-Budget Linking. In this paper, we present the data used and the results of the formal run.conference pape
Overview of the NTCIR-17 Lifelog-5 Task
Get PDFNTCIR-17 witnessed the fifth iteration of the Lifelog task, which was designed to facilitate the comparative evaluation of various approaches for automatic and interactive information retrieval from multimodal lifelog archives. Within this paper, we elucidate the utilization of the test collection, delineate the specified tasks, provide an overview of the submissions, and present the findings derived from the NTCIR17 Lifelog-5 LSAT sub-task. Our conclusion includes recommendations for potential future developments in the realm of lifelog tasks.conference pape
KASYS at the NTCIR-17 Transfer Task
Get PDFThis paper describes the KASYS team's participation in the NTCIR-17 Transfer Task. To generate our runs, we used neural IR models such as Contriever, ColBERT, and SPLADE with different fine-tuning strategies.conference pape
