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ResourceSync Framework Specification (ANSI/NISO Z39.99-2017)
Get PDF本アイテムは「ResourceSync Framework Specification (ANSI/NISO Z39.99-2017)」を国立情報学研究所で翻訳したものである。othe
THUIR at the NTCIR-16 WWW-4 Task
Get PDFThe THUIR team participates in the English subtask of the NTCIR-16 We Want Web with CENTRE(WWW-4) task. This paper elaborates on our methods and discusses the experimental results. We adopt three methods, namely learning-to-rank models, a pre-trained language model tailored for information retrieval, and BERT with prompt learning. Experimental results demonstrate the importance of designing pre-training task specifically for information retrieval. Results also suggest the relatively simple prompt method cannot effectively improve the ranking performance.conference pape
KSU Systems at the NTCIR-16 Data Search2 IR Subtask
Get PDFThis paper describes the system and results of Team KSU work on the NTCIR-16 Data Search2 IR subtask. The documents covered by this task consist of metadata extracted from the governmental statistical data and the body of the corresponding statistical data. The metadata is characterized by the fact that its document length is short, and the main body of statistical data is almost always composed of numbers, except for titles, headers, and comments. In the previous studies on ad hoc search for statistical documents, most of the ranking methods used only the metadata of the statistical documents, and there are few methods of using the contents of the tables of statistical data. However, ranking methods using only metadata have not been able to achieve the same or better performance compared to conventional ad hoc search for text documents. Therefore, in this paper, we propose a method that employs features of the table body of statistical data and a re-ranking method based on neural network models used in neural search, and verify how much the ranking results are improved. For the features of the main body of the table, we use eight types of features, four from the main body of the table and four from the whole table. As a neural search method, we use a re-ranking method based on the scores predicted from the features obtained by BERT and MLP. The results of the experiment showed that the method combining category search and BM25 resulted in nDCG@10 of 0.314 for Japanese and that of 0.069 for English. The results showed that Japanese ranked 2nd and English 6th among all teams.conference pape
Takelab at the NTCIR-16 QA Lab-PoliInfo-3 Task
Get PDFThis paper proposes a method for budget argument mining using topic extraction based on utterance classification. We employ a domain-specific word embedding, which is calculated only from the given data, to link budget descriptions with corresponding arguments.conference pape
AKBL at the NTCIR-16 QA Lab-PoliInfo-3 Task
Get PDFAKBL team participated in the QA alignment, the Question Answering, and the Fact Verification subtasks. For the QA alignment subtask, our method firstly divides given question and answer texts into semantically consistent segments, then apply the Hungarian algorithm with the BM25 similarity metric to align those segments. For the Question Answering subtask, our system firstly selects a short segment relevant to a given question summary from the answer text, then converts it into the answer summary by using the abstractive summarizer based on the pre-trained BART. For the Fact Verification subtask, our best system firstly retrieves a passage relevant to a given claim from the assembly minutes, then checks if the passage entails the claim or not by using a BERT-based textual entailment classifier.conference pape
NAISTSOC at the NTCIR-16 Real-MedNLP Task
Get PDFThis paper describes how we tackled the Medical Natural Language Processing for Real-MedNLP task as participants of NTCIR16. We utilized BERT model for solving this task. We found that BERT model we trained is the best results with F1-score.conference pape
RUCIR21 at the NTCIR-16 ULTRE Task
Get PDFThe RUCIR21 team participated in both the offline and online subtasks of the NTCIR-16 Unbiased Learning to Rank Evaluation (ULTRE) task. This paper describes our approaches and reports the results in the ULTRE task. In the offline subtask, we tried four learning to rank models based on Mobile Click Model (MCM), as well as a revived Dual Learning Algorithm (DLA) model. In the online subtask, we revived a Pairwise Differentiable Gradient Descent (PDGD) run and two online DLA runs, we also tried an online DLA model based on MCM.conference pape
第32回 これからの学術情報システム構築検討委員会 配付資料
Get PDF会議名：第32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：2022年1月26日（水）15:00～17:00conference outpu