Real-MedNLP: Overview of REAL document-based MEDical Natural Language Processing Task

Abstract

A standard dataset collection is essential for the development of information science. Particularly in the medical field, in which privacy protection is a critical issue, the importance of the dataset is significant. To discuss the validness of various methods, we build the clinical text dataset, Real-MedNLP, for multiple medical tasks. The goal of Real-MedNLP is threefold: (1) Real datasets: Previous medical shared tasks, MedNLP, MedNLP2, and MedNLPDoc, were based on the pseudo dataset, which was built from medical textbooks or dummy clinical texts. This task prepares real radiology and case reports. (2) Bilingual capability: Both English and Japanese data are handled. (3) Practicality: Both fundamental (named entity recognition) and applied practical tasks are handled. This study introduces the task setting of Real-MedNLP and submitted systems. The methods mostly share the common paradigm, which is based on a fundamental language model, such as BERT, aiming to separate the resource problems. Based on their results, this study discusses the feasibility of their approaches to bring us the future direction of medical NLP. Note that the Real-MedNLP is a shared task that handles real Japanese medical texts.

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