The 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.
SHAO et al. (Tue,) studied this question.