Leveraging Token-Based Concept Information and Data Augmentation in Few-Resource NER: ZuKyo-EN at the NTCIR-16 Real-MedNLP task

In this paper, we discuss our contribution to the NII Testbeds and Community for Information Access Research (NTCIR) - 16 Real- MedNLP shared task. Our team (ZuKyo) participated in the English subtask: Few-resource Named Entity Recognition. The main challenge in this low-resource task was a low number of training documents annotated with a high number of tags and attributes. For our submissions, we used different general and domain-specific transfer learning approaches in combination with multiple data augmentation methods. In addition, we experimented with models enriched with biomedical concepts encoded as token-based input feature

Approach for Named Entity Recognition and Case Identification Implemented by ZuKyo-JA Sub-team at the NTCIR-16 Real-MedNLP Task

In this NTCIR-16 Real-MedNLP shared task paper, we present the methods of the ZuKyo-JA subteam for solving the Japanese part of Subtask1 and Subtask3 (Subtask1-CR-JA, Subtask1-RR- JA, Subtask3-RR-JA). Our solution is based on a sliding- window approach using a Japanese BERT pre-trained masked- language model., which was used as a common architecture for addressing the specific subtasks. We additionally present a method that makes extensive use of medical knowledge for the same case identification subtask3-RR-JA