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第24回大学図書館と国立情報学研究所との連携・協力推進会議配布資料
Get PDF会議名：第24回大学図書館と国立情報学研究所との連携・協力推進会議
開催場所：オンライン
日時：2022年6月29日（水）14:00～15:45conference objec
第24回大学図書館と国立情報学研究所との連携・協力推進会議議事要旨
Get PDF会議名：第24回大学図書館と国立情報学研究所との連携・協力推進会議
開催場所：オンライン
日時：2022年6月29日（水）14:00～15:45conference objec
SPARC Japan セミナー2021 「研究データポリシーが目指すものとは」 電気通信大学が目指す共創進化スマート社会とそのScience2.0への展開 ドキュメント
Get PDFSPARC Japan セミナー2021「研究データポリシーが目指すものとは」
開催場所：オンライン開催
日時：2022年2月22日（火）13:00-16:55conference objec
TMUNLP at the NTCIR-16 FinNum-3 Task: Multi-task Learning on BERT for Claim Detection and Numeral Category Classification
Get PDFIn financial documents, numbers often contain important information in addition to textual data. As a result, understanding the relationship and meaning between these numbers and words is one of the current research and development directions. The main goal of the NTCIR-16 FinNum-3 Task is to understand the meaning of the numeral in the financial reports, which can further classify the category and the claim of the target numerals. We proposed a system that can predict two tasks at the same time in this paper. Our method shows that the ensemble fine-tuned BERT has the best performance for predicting the category and claim, which reached 94.67% micro F1-score for the numerical category classification, and 92.75% micro F1-score for the claim detection.conference pape
WUST at NTCIR-16 FinNum-3 Task
Get PDFThis article introduces how we deal with the FinNum-3 task of NTCIR16. In the FinNum-3 task, the relationship between a numeral and a given label is the object of classification. In one text, given a target numeral and its offset in the text, models need to judge whether the given target numeral is in-claim or out-of-claim. In the experiments, we use the BiLSTM architecture to detect the in-claim or out-of-claim of the target numeral in two kinds of financial texts.conference pape
DCU Team at the NTCIR-16 RCIR Task
Get PDFReading is one of the most common everyday activities. People read through most of their daily context such as during study or for entertainment in their spare time. Despite playing a critical role in our lives, there has been limited research on how people read and how it affects their level of understanding. The NTCIR-16 RCIR challenge is the first collaborative evaluation that aims to automatically measure the reading comprehension of a reader and integrate it as part of the information retrieval process. In this paper, we present our approach for the NTCIR-16 RCIR challenge, in which task participants are required to predict reading comprehension using eye movement signals of the readers. We utilised several conventional machine learning techniques to estimate the level of comprehension and combined it with a language model to perform text retrieval. Our extensive experiments, covering both subject-dependent and subject-independent scenarios, showed that our approach with fine-tuning obtained a Spearman’s coefficient of 0.5993 for the comprehension-evaluation task and nDCG at 0.7296 for the comprehension-based retrieval task.conference pape
KNUIR at the NTCIR-16 RCIR: Predicting Comprehension Level using Regression Models based on Eye-Tracking Metadata
Get PDFWe participated in the CET sub-task of the NTCIR-16 RCIR. In order to participate in the NTCIR-16 reading comprehension information retrieval (RCIR) CET sub-task, we adopted five regression models: Linear Regression, Random Forest Regressor, Gradient Boosting Regressor, eXtreme Gradient Boosting (XGB) Regressor, and Voting Regressor. We submitted the prediction results of test data to NTCIR- 16 and analyzed the obtained results. Throughout the analysis, we found that Gradient Boosting and Random Forest Regressor generally show better performance with Spearman’s rho of 0.53 and 0.57, respectively. In addition, the feature importance analysis indicated that each participant shows different eye-tracking tendencies for their reading comprehension. Findings in our work may bring insight into the understanding of human reading and information seeking processes with the help of eye-tracking systems by applying various regression models.conference pape
NTTD at the NTCIR-16 Real-MedNLP Task
Get PDFThe NTTD team participated in the Subtask1-CR-JA and Subtask1-RR-JA subtasks of the NTCIR-16 Real-MedNLP Task. This paper reports our approach to solving the NER (named entity recognition) problem when dealing with limited labeled medical documents. The documents are real Case-Report and Radiographic-Report data in Japanese. We first applied out recently developed annotation inconsistency detection tool to detect and correct inappropriate labels within the given training data. Then we applied data augmentation methods to create additional labeled data and combined the original and additional data as training data of our model. In this task, we fine-tuned Flair by the forementioned training data and acquired the results.conference pape
GunNLP at the NTCIR-16 Real-MedNLP Task: Collaborative filtering-based similar case identification method via structured data “case matrix”
Get PDFClinical text data are highly expected to be utilized directly for medical examination or diagnosis to support doctors’ practices. In this study, I propose a framework of similar case identification for radiology reports by structuring the reports into a “case matrix” and by applying a collaborative filtering algorithm.conference pape