MedNLPDoc: Japanese Shared Task for Clinical NLP
Due to the recent replacements of physical documents with electronic medical records (EMR), the importance of information processing in medical fields has been increased. We have been organizing the MedNLP task series in NTCIR-10 and 11. These workshops were the first shared tasks which attempt to evaluate technologies that retrieve important information from medical reports written in Japanese. In this report, we describe the NTCIR-12 MedNLPDoc task which is designed for more advanced and practical use for the medical fields. This task is considered as a multi-labeling task to a patient record. This report presents results of the shared task, discusses and illustrates remained issues in the medical natural language processing field.
Code (0)
등록된 구현이 없습니다.
Tasks
Information RetrievalSimilar Papers 제목 키워드 기반
Inference of ICD Codes from Japanese Medical Records by Searching Disease Names
Importance of utilizing medical information is getting increased as electronic health records (EHRs) are widely used nowadays. We aim to assign international standardized disease codes, ICD-10, to Japanese textual inform…
Information RetrievalTechnical Report: Small Language Model for Japanese Clinical and Medicine
This report presents a small language model (SLM) for Japanese clinical and medicine, named NCVC-slm-1. This 1B parameters model was trained using Japanese text classified to be of high-quality. Moreover, NCVC-slm-1 was …
Language ModelingLanguage ModellingSmall Language ModelKyoto University Participation to WAT 2017
We describe here our approaches and results on the WAT 2017 shared translation tasks. Following our good results with Neural Machine Translation in the previous shared task, we continue this approach this year, with incr…
Language ModelingLanguage ModellingMachine TranslationTranslationBering Lab’s Submissions on WAT 2021 Shared Task
This paper presents the Bering Lab’s submission to the shared tasks of the 8th Workshop on Asian Translation (WAT 2021) on JPC2 and NICT-SAP. We participated in all tasks on JPC2 and IT domain tasks on NICT-SAP. Our appr…
NMTSentenceTranslationSupervised neural machine translation based on data augmentation and improved training \& inference process
This is the second time for SRCB to participate in WAT. This paper describes the neural machine translation systems for the shared translation tasks of WAT 2019. We participated in ASPEC tasks and submitted results on En…
Data AugmentationMachine TranslationPositionTranslation